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f:{"text":"Overview of Sora","id":"overview-of-sora","headlineTag":"h2","omitFromTableOfContents":true}
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11:{"text":"Pretraining filtering and Data Preprocessing","id":"pretraining-filtering-and-data-preprocessing","headlineTag":"h2","omitFromTableOfContents":true}
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17:{"text":"Sora Mitigation Stack","id":"sora-mitigation-stack","headlineTag":"h2","omitFromTableOfContents":false}
18:{"text":"System and Model Mitigations","id":"system-and-model-mitigations","headlineTag":"h2","omitFromTableOfContents":true}
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20:{"text":"Likeness pilot","id":"likeness-pilot","headlineTag":"h2","omitFromTableOfContents":true}
21:{"text":"Provenance and transparency Initiatives","id":"provenance-and-transparency-initiatives","headlineTag":"h2","omitFromTableOfContents":true}
22:{"text":"Expanding representation in our outputs","id":"expanding-representation-in-our-outputs","headlineTag":"h2","omitFromTableOfContents":true}
23:{"text":"Continued safety, policy, and ethical alignment","id":"continued-safety-policy-and-ethical-alignment","headlineTag":"h2","omitFromTableOfContents":true}
24:{"text":"Acknowledgements","id":"acknowledgements","headlineTag":"h2","omitFromTableOfContents":false}
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Users can create videos up to 1080p resolution (20 seconds max) in various formats, generate new content from text, or enhance, remix, and blend their own assets. Users will be able to explore the Featured and Recent feeds which showcase community creations and offer inspiration for new ideas. Sora builds on learnings from DALL·E and GPT models, and is designed to give people expanded tools for storytelling and creative expression. "]}]]}]]}],["$","p","1",{"className":"mb-sm last:mb-0","children":[["$","$2e","0",{"children":[["$","span","text-end-0",{"children":["Sora is a diffusion model, which generates a video by starting off with a base video that looks like static noise and gradually transforms it by removing the noise over many steps. By giving the model foresight of many frames at a time, we’ve solved a challenging problem of making sure a subject stays the same even when it goes out of view temporarily. Similar to GPT models, Sora uses a transformer architecture, unlocking superior scaling performance. "]}]]}]]}],["$","p","2",{"className":"mb-sm last:mb-0","children":[["$","$2e","0",{"children":[["$","span","text-end-0",{"children":["Sora uses the recaptioning technique from DALL·E 3, which involves generating highly descriptive captions for the visual training data. As a result, the model is able to follow the user’s text instructions in the generated video more faithfully."]}]]}]]}],["$","p","3",{"className":"mb-sm last:mb-0","children":[["$","$2e","0",{"children":[["$","span","text-end-0",{"children":["In addition to being able to generate a video solely from text instructions, the model is able to take an existing still image and generate a video from it, animating the image’s contents with accuracy and attention to small detail. The model can also take an existing video and extend it or fill in missing frames⁠. Sora serves as a foundation for models that can understand and simulate the real world, a capability we believe will be an important milestone for achieving AGI."]}]]}]]}],["$","p","4",{"className":"mb-sm last:mb-0","children":[["$","$2e","0",{"children":[["$","span","text-end-0",{"children":["Sora’s capabilities may also introduce novel risks, such as the potential for misuse of likeness or the generation of misleading or explicit video content. In order to safely deploy Sora in a product, we built on learnings from safety work for DALL·E’s deployment in ChatGPT and the API and safety mitigations for other OpenAI products such as ChatGPT. This system card outlines the resulting mitigation stack, external red teaming efforts, evaluations, and ongoing research to refine these safeguards further."]}]]}]]}]]}]}]]}]}],["$","div","3wjw4rwam93lPo5QJDbYTo",{"className":"@md:mt-[3.5rem] mt-[2rem] @md:-mb-[2.5rem] -mb-[1.75rem]","children":["$","$25",null,{"fallback":null,"children":[["$","$L26",null,{"moduleIds":"$2c"}],"$L2f"]}]}],["$","div","3RXQIudPy7ptPaYVR0v49r",{"className":"","children":["$","$25",null,{"fallback":null,"children":[["$","$L26",null,{"moduleIds":"$30"}],["$","div",null,{"className":"w-full grid grid-cols-12 @container max-w-container multi-columns:px-0 multi-columns:flex","children":["$","div",null,{"className":"col-span-12 @md:col-span-6 @md:col-start-4 full-grid-content:@md:col-span-12 full-grid-content:@md:col-start-1 max-w-none prose","children":[["$","p","0",{"className":"mb-sm last:mb-0","children":[["$","$2e","0",{"children":[["$","span","text-end-0",{"children":["As described in our "]}]]}],["$","$L27","1",{"href":"https://openai.com/index/video-generation-models-as-world-simulators/","external":false,"className":"underline-offset-[0.25rem] underline decoration-1 hover:text-primary-60 text-primary-100","children":[[["$","u","0",{"children":[["$","span","text-end-0",{"children":["technical report"]}]]}]],"⁠"]}],["$","$2e","2",{"children":[["$","sup","sup-882",{"children":["$","$L31","0",{"number":"1"}]}],["$","span","text-end-8",{"children":[" from February 2024, Sora takes inspiration from large language models which acquire generalist capabilities by training on internet-scale data. The success of the LLM paradigm is enabled in part by the use of tokens that elegantly unify diverse modalities of text—code, math and various natural languages. With Sora, we considered how generative models of visual data can inherit such benefits. Whereas LLMs have text tokens, Sora has visual patches. Patches have previously been shown to be an effective representation for models of visual data. We found that patches are a highly-scalable and effective representation for training generative models on diverse types of videos and images. At a high level, we turn videos into patches by first compressing videos into a lower-dimensional latent space, and subsequently decomposing the representation into spacetime patches."]}]]}]]}],["$","p","1",{"className":"mb-sm last:mb-0","children":[["$","$2e","0",{"children":[["$","span","text-end-0",{"children":["Sora was trained on diverse datasets, including a mix of publicly available data, proprietary data accessed through partnerships, and custom datasets developed in-house. These consist of:"]}]]}]]}],["$","ul","2",{"className":"mb-md marker:text-inherit last:mb-0 list-disc pl-2xs mx-3xs","children":[["$","li","0",{"className":"mb-4xs last:mb-0","children":[["$","b",".$0",{"children":[["$","span","text-end-0",{"children":["Select publicly available data"]}]]}],["$","$2e",".$1",{"children":[["$","span","text-end-0",{"children":[", mostly collected from industry-standard machine learning datasets and web crawls."]}]]}]]}],["$","li","1",{"className":"mb-4xs last:mb-0","children":[["$","b",".$0",{"children":[["$","span","text-end-0",{"children":["Proprietary data from data partnerships"]}]]}],["$","$2e",".$1",{"children":[["$","span","text-end-0",{"children":[". We form partnerships to access non-publicly available data. For example, we partnered with Shutterstock⁠ Pond5 on building and delivering AI-generated images. We also partner to commission and create datasets fit for our needs."]}]]}]]}],["$","li","2",{"className":"mb-4xs last:mb-0","children":[["$","b",".$0",{"children":[["$","span","text-end-0",{"children":["Human data:"]}]]}],["$","$2e",".$1",{"children":[["$","span","text-end-0",{"children":[" Feedback from AI trainers, red teamers, and employees. "]}]]}]]}]]}],null]}]}]]}]}],["$","div","6h9RUgF6wolZrLAAoRNvpZ",{"className":"@md:mt-[3.5rem] mt-[2rem] @md:-mb-[2.5rem] -mb-[1.75rem]","children":["$","$25",null,{"fallback":null,"children":[["$","$L26",null,{"moduleIds":"$2c"}],"$L32"]}]}],["$","div","37VB23npLr14ZprrDAizaO",{"className":"","children":["$","$25",null,{"fallback":null,"children":[["$","$L26",null,{"moduleIds":"$30"}],["$","div",null,{"className":"w-full grid grid-cols-12 @container max-w-container multi-columns:px-0 multi-columns:flex","children":["$","div",null,{"className":"col-span-12 @md:col-span-6 @md:col-start-4 full-grid-content:@md:col-span-12 full-grid-content:@md:col-start-1 max-w-none prose","children":[["$","p","0",{"className":"mb-sm last:mb-0","children":[["$","$2e","0",{"children":[["$","span","text-end-0",{"children":["In addition to mitigations implemented after the pre-training stage, pre-training filtering mitigations can provide an additional layer of defense that, along with other safety mitigations, help exclude unwanted and harmful data from our datasets. Before training, all datasets thus undergo this filtering process, removing the most explicit, violent, or otherwise sensitive content (for instance, some hate symbols), representing an extension of the methods used to filter the data on which we trained our other models, including DALL·E 2 and DALL·E 3."]}]]}]]}]]}]}]]}]}],["$","div","xPsIk8bjqU9ifs7z1rSGA",{"className":"@md:mt-[3.5rem] mt-[2rem] @md:-mb-[2.5rem] -mb-[1.75rem]","children":["$","$25",null,{"fallback":null,"children":[["$","$L26",null,{"moduleIds":"$2c"}],"$L33"]}]}],["$","div","3RAbAAEugKPA2OqeOGwrZO",{"className":"","children":["$","$25",null,{"fallback":null,"children":[["$","$L26",null,{"moduleIds":"$30"}],["$","div",null,{"className":"w-full grid grid-cols-12 @container max-w-container multi-columns:px-0 multi-columns:flex","children":["$","div",null,{"className":"col-span-12 @md:col-span-6 @md:col-start-4 full-grid-content:@md:col-span-12 full-grid-content:@md:col-start-1 max-w-none prose","children":[["$","p","0",{"className":"mb-sm last:mb-0","children":[["$","$2e","0",{"children":[["$","span","text-end-0",{"children":["We undertook a robust process to understand both potential misuse and real-world creative uses to help inform Sora’s designs and safety mitigations. After the Sora announcement in February of 2024, we worked with hundreds of visual artists, designers, and filmmakers from more than 60 countries to gain feedback on how to advance the model to be most helpful for creative professionals. We also crafted a number of evaluations internally and with external red-teamers to discover and assess risks and iteratively improve our safety and risk mitigations. "]}]]}]]}],["$","p","1",{"className":"mb-sm last:mb-0","children":[["$","$2e","0",{"children":[["$","span","text-end-0",{"children":["Our safety stack for Sora builds on these learnings and on existing safety mitigations we employ in other models and products such as DALL·E and ChatGPT, as well as custom-built mitigations specific to our video product. Because this is a powerful tool, we are taking an iterative approach to safety, particularly in areas where context is important or we foresee novel risks related to video. Examples of our iterative approach include age gating access to users who are 18 or older, restricting the use of likeness/face-uploads, and having more conservative moderation thresholds on prompts and uploads of minors at launch. We want to continue to learn how people use Sora and iterate to best balance safety while maximizing creative potential for our users. "]}]]}]]}]]}]}]]}]}],["$","div","2UTY5JrxBRQugJjTMCvDYp",{"className":"@md:mt-[3.5rem] mt-[2rem] @md:-mb-[2.5rem] -mb-[1.75rem]","children":["$","$25",null,{"fallback":null,"children":[["$","$L26",null,{"moduleIds":"$2c"}],"$L34"]}]}],["$","div","2ZLCpR5VnfIEvVnEx3KLPB",{"className":"","children":["$","$25",null,{"fallback":null,"children":[["$","$L26",null,{"moduleIds":"$30"}],["$","div",null,{"className":"w-full grid grid-cols-12 @container max-w-container multi-columns:px-0 multi-columns:flex","children":["$","div",null,{"className":"col-span-12 @md:col-span-6 @md:col-start-4 full-grid-content:@md:col-span-12 full-grid-content:@md:col-start-1 max-w-none prose","children":[["$","p","0",{"className":"mb-sm last:mb-0","children":[["$","$2e","0",{"children":[["$","span","text-end-0",{"children":["OpenAI worked with external red teamers located in nine different countries to test Sora, identify weaknesses in the safety mitigations, and give feedback on risks associated with Sora’s new product capabilities. Red teamers had access to the Sora product with various iterations of safety mitigations and system maturity starting in September and continuing into December 2024, testing more than 15,000 generations. This red teaming effort builds upon work in early 2024 where a Sora model without production mitigations was tested."]}]]}]]}],["$","p","1",{"className":"mb-sm last:mb-0","children":[["$","$2e","0",{"children":[["$","span","text-end-0",{"children":["Red teamers explored novel potential risks of Sora’s model and the product’s tools, and tested safety mitigations as they were developed and improved. These red teaming campaigns covered various types of violative and disallowed content (sexual and erotic content, violence and gore, self harm, illegal content, mis/disinformation, etc), adversarial tactics (both prompting and tool/feature use) to evade safety mitigations, as well as how these tools could be exploited to progressively degrade moderation tools and safeguards. Red teamers also provided feedback on their perceptions of Sora on areas including bias and general performance. "]}]]}]]}],["$","p","2",{"className":"mb-sm last:mb-0","children":[["$","$2e","0",{"children":[["$","span","text-end-0",{"children":["We explored text-to-video generation using both straightforward prompts and adversarial prompting tactics across all content categories mentioned above. The media upload capability was tested with a large variety of images and videos, including public persons, and a broad variety of content categories to test the ability to generate violative content. We also tested various uses and combinations of the modification tools (storyboards, recut, remix, and blend) to assess their utility to generate prohibited content. "]}]]}]]}],["$","p","3",{"className":"mb-sm last:mb-0","children":[["$","$2e","0",{"children":[["$","span","text-end-0",{"children":["Red teamers identified noteworthy observations for both specific types of prohibited content and general adversarial tactics. For example, red teamers found that using text prompts with either medical situations or science fiction / fantasy settings degraded safeguards against generating erotic and sexual content until additional mitigations were built. Red teamers used adversarial tactics to evade elements of the safety stack, including suggestive prompts and using metaphors to harness the model’s inference capability. Over many attempts, they could identify trends of prompts and words which would trigger safeguards, and test different phrasing and words to evade refusals. Red teamers would eventually select the most-concerning generation to use as seed media for further development into violative content that couldn’t be created with single-prompt techniques. Jailbreak techniques sometimes proved effective to degrade safety policies, allowing us to refine these protections as well."]}]]}]]}],["$","p","4",{"className":"mb-sm last:mb-0","children":[["$","$2e","0",{"children":[["$","span","text-end-0",{"children":["Red teamers also tested media uploads and Sora’s tools (storyboards, recut, remix, and blend) with both publicly available images and AI-generated media. This revealed gaps in input and output filtering to strengthen prior to Sora’s release, and helped hone protections for media uploads including people. Testing also revealed the need for stronger classifier filtering to mitigate the risk of non-violative media uploads being modified into prohibited erotic, violence, or deepfake content."]}]]}]]}],["$","p","5",{"className":"mb-sm last:mb-0","children":[["$","$2e","0",{"children":[["$","span","text-end-0",{"children":["The feedback and data generated by red teamers enabled the creation of additional layers of safety mitigations and improvements on existing safety evaluations, which are described in the Specific Risk Areas and Mitigations⁠ sections. These efforts allowed additional tuning of our prompt filtering, blocklists, and classifier thresholds to ensure model compliance with safety goals."]}]]}]]}]]}]}]]}]}],["$","div","72tjLgn9QExk927OsZNGgS",{"className":"@md:mt-[3.5rem] mt-[2rem] @md:-mb-[2.5rem] -mb-[1.75rem]","children":["$","$25",null,{"fallback":null,"children":[["$","$L26",null,{"moduleIds":"$2c"}],"$L35"]}]}],["$","div","5MnkFVw27XB4DYgMkvPQrN",{"className":"","children":["$","$25",null,{"fallback":null,"children":[["$","$L26",null,{"moduleIds":"$30"}],["$","div",null,{"className":"w-full grid grid-cols-12 @container max-w-container multi-columns:px-0 multi-columns:flex","children":["$","div",null,{"className":"col-span-12 @md:col-span-6 @md:col-start-4 full-grid-content:@md:col-span-12 full-grid-content:@md:col-start-1 max-w-none prose","children":[["$","p","0",{"className":"mb-sm last:mb-0","children":[["$","$2e","0",{"children":[["$","span","text-end-0",{"children":["Over the last nine months, we observed user feedback across 500,000+ model requests from 300+ users from 60+ countries. This data informed enhancements in model behavior and model adherence to safety protocols. For example, artist feedback helped us understand the limitations a visible watermark has on their workflows, which informed our decision to allow paying users to download video files without the visible watermark while still embedding C2PA data. "]}]]}]]}],["$","p","1",{"className":"mb-sm last:mb-0","children":[["$","$2e","0",{"children":[["$","span","text-end-0",{"children":["This early access program also taught us that if Sora is intended to serve as an expanded tool for storytelling and creative expression, it would require us to offer more flexibility to artists around some sensitive areas we’d handle differently in a general-purpose tool like ChatGPT. We expect artists, independent filmmakers, studios and other entertainment industry organizations to use Sora as a crucial part of their development processes. At the same time, identifying both positive use cases and potential misuse allowed us to determine areas where more restrictive product-level mitigations were required to mitigate the risk of harm or misuse."]}]]}]]}]]}]}]]}]}],["$","div","6mDk0nHHtvAhZD28MDe7LU",{"className":"@md:mt-[3.5rem] mt-[2rem] @md:-mb-[2.5rem] -mb-[1.75rem]","children":["$","$25",null,{"fallback":null,"children":[["$","$L26",null,{"moduleIds":"$2c"}],"$L36"]}]}],["$","div","2vaqh64VsXBPirE5gSwgrZ",{"className":"","children":["$","$25",null,{"fallback":null,"children":[["$","$L26",null,{"moduleIds":"$30"}],["$","div",null,{"className":"w-full grid grid-cols-12 @container max-w-container multi-columns:px-0 multi-columns:flex","children":["$","div",null,{"className":"col-span-12 @md:col-span-6 @md:col-start-4 full-grid-content:@md:col-span-12 full-grid-content:@md:col-start-1 max-w-none prose","children":[["$","p","0",{"className":"mb-sm last:mb-0","children":[["$","$2e","0",{"children":[["$","span","text-end-0",{"children":["We developed internal evaluations targeting key areas, including nudity, deceptive election content, self-harm, and violence. These evaluations were designed to support the refinement of mitigations and help inform our moderation thresholds. The evaluation framework combines input prompts given to the video generation model with input and output classifiers applied to either transformed prompts or the final produced videos."]}]]}]]}],["$","p","1",{"className":"mb-sm last:mb-0","children":[["$","$2e","0",{"children":[["$","span","text-end-0",{"children":["The input prompts for these evaluations were sourced from three primary channels: data collected during the early alpha phase (as outlined in Section 3.2), adversarial examples provided by red-team testers (referenced in Section 3.1), and synthetic data generated using GPT‑4. Alpha phase data provided insight into real-world usage scenarios, red-teamer contributions helped uncover adversarial and edge-case content, and synthetic data allowed for expanding evaluation sets in areas like unintended racy content, where naturally occurring examples are scarce."]}]]}]]}]]}]}]]}]}],["$","div","7hrXkRknx8zNum6dAYykqU",{"className":"@md:mt-[3.5rem] mt-[2rem] @md:-mb-[2.5rem] -mb-[1.75rem]","children":["$","$25",null,{"fallback":null,"children":[["$","$L26",null,{"moduleIds":"$2c"}],"$L37"]}]}],["$","div","uv4ZTArvPOWmCiAxsqTgR",{"className":"","children":["$","$25",null,{"fallback":null,"children":[["$","$L26",null,{"moduleIds":"$30"}],["$","div",null,{"className":"w-full grid grid-cols-12 @container max-w-container multi-columns:px-0 multi-columns:flex","children":["$","div",null,{"className":"col-span-12 @md:col-span-6 @md:col-start-4 full-grid-content:@md:col-span-12 full-grid-content:@md:col-start-1 max-w-none prose","children":[["$","p","0",{"className":"mb-sm last:mb-0","children":[["$","$2e","0",{"children":[["$","span","text-end-0",{"children":["The preparedness framework is designed to evaluate whether frontier model capabilities introduce significant risks in four tracked categories: persuasion, cybersecurity, CBRN (chemical, biological, radiological, and nuclear), and model autonomy. We do not have evidence that Sora poses any significant risk with respect to cybersecurity, CBRN, or model autonomy. These risks are closely tied to models that interact with computer systems, scientific knowledge, or autonomous decision-making, all of which are currently beyond Sora’s scope as a video-generation tool. "]}]]}]]}],["$","p","1",{"className":"mb-sm last:mb-0","children":[["$","$2e","0",{"children":[["$","span","text-end-0",{"children":["Sora’s video generation capabilities could pose potential risk from persuasion, such as risks of impersonation, misinformation, or social engineering. To address these risks, we have developed a suite of mitigations that are described in the below sections.  These include mitigations intended to prevent the generation of likeness to well-known public figures. Additionally, given that context and the knowledge of a video being real or AI-generated may be key in determining how persuasive a generated video is, we’ve focused on building a multi-layered provenance approach, including metadata, watermarks, and fingerprinting."]}]]}]]}]]}]}]]}]}],["$","div","4QxcKaLKbYQsoNvMDtbKaj",{"className":"@md:mt-[3.5rem] mt-[2rem] @md:-mb-[2.5rem] -mb-[1.75rem]","children":["$","$25",null,{"fallback":null,"children":[["$","$L26",null,{"moduleIds":"$2c"}],"$L38"]}]}],["$","div","64e3qL5JTc3fQe32rP2qLo",{"className":"","children":["$","$25",null,{"fallback":null,"children":[["$","$L26",null,{"moduleIds":"$30"}],["$","div",null,{"className":"w-full grid grid-cols-12 @container max-w-container multi-columns:px-0 multi-columns:flex","children":["$","div",null,{"className":"col-span-12 @md:col-span-6 @md:col-start-4 full-grid-content:@md:col-span-12 full-grid-content:@md:col-start-1 max-w-none prose","children":[["$","p","0",{"className":"mb-sm last:mb-0","children":[["$","$2e","0",{"children":[["$","span","text-end-0",{"children":["In addition to the specific risks and mitigations identified below, choices made in Sora’s training, product design, and policies help to broadly mitigate the risk of harmful or unwanted outputs. These can broadly be organized into system and model-level technical mitigations, as well as product policies and user education."]}]]}]]}]]}]}]]}]}],["$","div","5JR7m9xqy7OqmTfeDjfFjG",{"className":"@md:mt-[3.5rem] mt-[2rem] @md:-mb-[2.5rem] -mb-[1.75rem]","children":["$","$25",null,{"fallback":null,"children":[["$","$L26",null,{"moduleIds":"$2c"}],"$L39"]}]}],["$","div","5cNkJTCtvElioDg4soU2Pe",{"className":"","children":["$","$25",null,{"fallback":null,"children":[["$","$L26",null,{"moduleIds":"$30"}],["$","div",null,{"className":"w-full grid grid-cols-12 @container max-w-container multi-columns:px-0 multi-columns:flex","children":["$","div",null,{"className":"col-span-12 @md:col-span-6 @md:col-start-4 full-grid-content:@md:col-span-12 full-grid-content:@md:col-start-1 max-w-none prose","children":[["$","p","0",{"className":"mb-sm last:mb-0","children":[["$","$2e","0",{"children":[["$","span","text-end-0",{"children":["Below we detail the primary forms of safety mitigations we have in place before a user is shown their requested output:"]}]]}]]}],["$","p","1",{"className":"mb-sm last:mb-0","children":[["$","b","0",{"children":[["$","span","text-end-0",{"children":["Text and image moderation via multi-modal moderation classifier"]}]]}]]}],["$","p","2",{"className":"mb-sm last:mb-0","children":[["$","$2e","0",{"children":[["$","span","text-end-0",{"children":["Our multi-modal moderation classifier powering our external Moderation API is applied to identify text, image or video prompts that may violate our usage policies, both on input and outputs. Violative prompts detected by the system will result in a refusal. "]}]]}],["$","$L27","1",{"href":"https://openai.com/index/upgrading-the-moderation-api-with-our-new-multimodal-moderation-model/","external":false,"className":"underline-offset-[0.25rem] underline decoration-1 hover:text-primary-60 text-primary-100","children":[[["$","u","0",{"children":[["$","span","text-end-0",{"children":["Learn more about our multi-modal moderation API here"]}]]}]],"⁠"]}],["$","$2e","2",{"children":[["$","span","text-0",{"children":["."]}],["$","sup","sup-9",{"children":["$","$L31","0",{"number":"2"}]}]]}]]}],["$","p","3",{"className":"mb-sm last:mb-0","children":[["$","b","0",{"children":[["$","span","text-end-0",{"children":["Custom LLM filtering"]}]]}]]}],["$","p","4",{"className":"mb-sm last:mb-0","children":[["$","$2e","0",{"children":[["$","span","text-end-0",{"children":["One advantage of video generation technology is the ability to perform asynchronous moderation checks without adding latency to the overall user experience. Since video generation inherently takes a few seconds to process, this window of time can be utilized to run precision-targeted moderation checks. We have customized our own GPT to achieve high precision on the moderation for some specific topics, including identifying third-party content as well as deceptive content. "]}]]}]]}],["$","p","5",{"className":"mb-sm last:mb-0","children":[["$","$2e","0",{"children":[["$","span","text-end-0",{"children":["Filters are multimodal: both image/video uploads,text prompts and outputs are included in the context of each LLM call. This allows us to detect violating combinations across image and text. "]}]]}]]}],["$","p","6",{"className":"mb-sm last:mb-0","children":[["$","b","0",{"children":[["$","span","text-end-0",{"children":["Image output classifiers"]}]]}]]}],["$","p","7",{"className":"mb-sm last:mb-0","children":[["$","$2e","0",{"children":[["$","span","text-end-0",{"children":["To address potentially harmful content directly in outputs, Sora uses output classifiers, including specialized filters for NSFW content, minors, violence, and potential misuse of likeness. Sora may block videos before they are shared with the user if these classifiers are activated. "]}]]}]]}],["$","p","8",{"className":"mb-sm last:mb-0","children":[["$","b","0",{"children":[["$","span","text-end-0",{"children":["Blocklists"]}]]}]]}],["$","p","9",{"className":"mb-sm last:mb-0","children":[["$","$2e","0",{"children":[["$","span","text-end-0",{"children":["We maintain textual blocklists across a variety of categories, informed by our previous work on DALL·E 2 and DALL·E 3, proactive risk discovery, and results from early users."]}]]}]]}]]}]}]]}]}],["$","div","55f4mEMWDhtNOqaOIYloO7",{"className":"@md:mt-[3.5rem] mt-[2rem] @md:-mb-[2.5rem] -mb-[1.75rem]","children":["$","$25",null,{"fallback":null,"children":[["$","$L26",null,{"moduleIds":"$2c"}],"$L3a"]}]}],["$","div","7d5bS9H9hTgPE1X1dq5KWe",{"className":"","children":["$","$25",null,{"fallback":null,"children":[["$","$L26",null,{"moduleIds":"$30"}],["$","div",null,{"className":"w-full grid grid-cols-12 @container max-w-container multi-columns:px-0 multi-columns:flex","children":["$","div",null,{"className":"col-span-12 @md:col-span-6 @md:col-start-4 full-grid-content:@md:col-span-12 full-grid-content:@md:col-start-1 max-w-none prose","children":[["$","p","0",{"className":"mb-sm last:mb-0","children":[["$","$2e","0",{"children":[["$","span","text-end-0",{"children":["In addition to the protections we have built into the model and system to prevent the generation of violative content, we are also taking additional steps to reduce the risk of misuse. We currently are only offering Sora to users who are 18 or older and we are applying moderation filters to the content that is shown in the Explore and Featured feeds."]}]]}]]}],["$","p","1",{"className":"mb-sm last:mb-0","children":[["$","$2e","0",{"children":[["$","span","text-end-0",{"children":["We are also clearly communicating policy guidelines through in-product and publicly available education on:"]}]]}]]}],["$","ul","2",{"className":"mb-md marker:text-inherit last:mb-0 list-disc pl-2xs mx-3xs","children":[["$","li","0",{"className":"mb-4xs last:mb-0","children":[["$","$2e",".$0",{"children":[["$","span","text-end-0",{"children":["Use of another person’s likeness without their permission, and a prohibition on depicting real minors;"]}]]}]]}],["$","li","1",{"className":"mb-4xs last:mb-0","children":[["$","$2e",".$0",{"children":[["$","span","text-end-0",{"children":["Creating illegal content or content that violates intellectual property rights;"]}]]}]]}],["$","li","2",{"className":"mb-4xs last:mb-0","children":[["$","$2e",".$0",{"children":[["$","span","text-end-0",{"children":["The generation of explicit and harmful content, such as non-consensual intimate imagery, content used to bully, harass, or defame, or content intended to promote violence, hatred, or the suffering of others; and "]}]]}]]}],["$","li","3",{"className":"mb-4xs last:mb-0","children":[["$","$2e",".$0",{"children":[["$","span","text-end-0",{"children":["The creation and distribution of content used to defraud, scam, or mislead others."]}]]}]]}]]}],["$","p","3",{"className":"mb-sm last:mb-0","children":[["$","$2e","0",{"children":[["$","span","text-end-0",{"children":["Some of these forms of misuse are addressed through our model and system mitigations, but others are more contextual—a scene of a protest can be used for legitimate creative endeavors, but the same scene presented as a real current event could also be shared as disinformation if paired with other claims. "]}]]}]]}],["$","p","4",{"className":"mb-sm last:mb-0","children":[["$","$2e","0",{"children":[["$","span","text-end-0",{"children":["Sora is designed to give people the ability to express a wide range of creative ideas and views. It is not practical nor advisable to prevent every form of contextually problematic content."]}]]}]]}],["$","p","5",{"className":"mb-sm last:mb-0","children":[["$","$2e","0",{"children":[["$","span","text-end-0",{"children":["We offer people the ability to "]}]]}],["$","$L27","1",{"href":"https://openai.com/form/report-content/","external":false,"className":"underline-offset-[0.25rem] underline decoration-1 hover:text-primary-60 text-primary-100","children":[[["$","u","0",{"children":[["$","span","text-end-0",{"children":["report"]}]]}]],"⁠"]}],["$","$2e","2",{"children":[["$","span","text-end-0",{"children":[" Sora videos they think may violate our guidelines while leveraging automation and human review to actively monitor patterns of use. We have established enforcement mechanisms to remove violative videos and penalize users. When users do violate our guidelines, we will notify them and offer the opportunity to tell us what they think is fair. We intend to track the effectiveness of these mitigations and refine them over time."]}]]}]]}]]}]}]]}]}],["$","div","77LyJHIBx3ghHbx46yx84o",{"className":"@md:mt-[3.5rem] mt-[2rem] @md:-mb-[2.5rem] -mb-[1.75rem]","children":["$","$25",null,{"fallback":null,"children":[["$","$L26",null,{"moduleIds":"$2c"}],"$L3b"]}]}],["$","div","5Ph1bqNWcLtur9vuTQ6B5W",{"className":"","children":["$","$25",null,{"fallback":null,"children":[["$","$L26",null,{"moduleIds":"$30"}],["$","div",null,{"className":"w-full grid grid-cols-12 @container max-w-container multi-columns:px-0 multi-columns:flex","children":["$","div",null,{"className":"col-span-12 @md:col-span-6 @md:col-start-4 full-grid-content:@md:col-span-12 full-grid-content:@md:col-start-1 max-w-none prose","children":[["$","p","0",{"className":"mb-sm last:mb-0","children":[["$","$2e","0",{"children":[["$","span","text-end-0",{"children":["Beyond general safety measures above, early testing and evaluation helped identify several areas of particular safety focus. "]}]]}]]}]]}]}]]}]}],["$","div","1VFHoAiAvndzM6zn0jrGri",{"className":"@md:mt-[3.5rem] mt-[2rem] @md:-mb-[2.5rem] -mb-[1.75rem]","children":["$","$25",null,{"fallback":null,"children":[["$","$L26",null,{"moduleIds":"$2c"}],"$L3c"]}]}],["$","div","7zAEF0ycy8vIOCow6WOR3x",{"className":"","children":["$","$25",null,{"fallback":null,"children":[["$","$L26",null,{"moduleIds":"$30"}],["$","div",null,{"className":"w-full grid grid-cols-12 @container max-w-container multi-columns:px-0 multi-columns:flex","children":["$","div",null,{"className":"col-span-12 @md:col-span-6 @md:col-start-4 full-grid-content:@md:col-span-12 full-grid-content:@md:col-start-1 max-w-none prose","children":[["$","p","0",{"className":"mb-sm last:mb-0","children":[["$","$2e","0",{"children":[["$","span","text-end-0",{"children":["OpenAI is "]}]]}],["$","$L27","1",{"href":"/index/child-safety-adopting-sbd-principles/","external":false,"className":"underline-offset-[0.25rem] underline decoration-1 hover:text-primary-60 text-primary-100","children":[[["$","u","0",{"children":[["$","span","text-end-0",{"children":["deeply committed to addressing"]}]]}]],"⁠"]}],["$","$2e","2",{"children":[["$","sup","sup-9",{"children":["$","$L31","0",{"number":"3"}]}],["$","span","text-end-8",{"children":[" "]}]]}],["$","sup","3",{"children":[["$","span","text-end-0",{"children":[" "]}]]}],["$","$2e","4",{"children":[["$","span","text-end-0",{"children":["child safety risks, and we prioritize prevention, detection, and reporting of "]}]]}],["$","$L27","5",{"href":"https://www.justice.gov/d9/2023-06/child_sexual_abuse_material_2.pdf","external":true,"className":"underline-offset-[0.25rem] underline decoration-1 hover:text-primary-60 text-primary-100","children":[[["$","$2e","0",{"children":[["$","span","text-end-0",{"children":["Child Sexual Abuse Material"]}]]}]],"⁠"]}],["$","$2e","6",{"children":[["$","span","text-0",{"children":[" (CSAM) content across all our products, including Sora. OpenAI efforts in the child safety space include responsibly sourcing our data sets to protect them from CSAM, partnering with National Center for Missing & Exploited Children (NCMEC) to prevent child sexual abuse and protect children, red-teaming in accordance with Thorn’s recommendations and in compliance with legal restrictions, and robust scanning for CSAM across all inputs and outputs. This includes scanning first party and third party users (API and Enterprise) unless customers meet rigorous criteria for removal of CSAM scanning. To prevent generation of CSAM, we have built a robust safety stack, leveraging system mitigations we use across our other products such as ChatGPT and DALL·E"]}],["$","sup","sup-836",{"children":["$","$L31","0",{"number":"4"}]}],["$","span","text-end-764",{"children":["  as well as some additional levers that we built specifically for Sora."]}]]}]]}],["$","p","1",{"className":"mb-sm last:mb-0","children":[["$","b","0",{"children":[["$","span","text-end-0",{"children":["Input Classifiers"]}]]}]]}],["$","p","2",{"className":"mb-sm last:mb-0","children":[["$","$2e","0",{"children":[["$","span","text-end-0",{"children":["For Child Safety we leverage 3 different input mitigations across text, image and video input: "]}]]}]]}],["$","ul","3",{"className":"mb-md marker:text-inherit last:mb-0 list-disc pl-2xs mx-3xs","children":[["$","li","0",{"className":"mb-4xs last:mb-0","children":[["$","$2e",".$0",{"children":[["$","span","text-end-0",{"children":["For all image and video uploads, we integrate with Safer, developed by Thorn, to detect matches with known CSAM. Confirmed matches are rejected and reported to NCMEC. Additionally, we utilize Thorn’s CSAM classifier to identify potentially new, unhashed CSAM content."]}]]}]]}],["$","li","1",{"className":"mb-4xs last:mb-0","children":[["$","$2e",".$0",{"children":[["$","span","text-end-0",{"children":["We leverage a multi-modal moderation classifier to detect and moderate any sexual content that involves minors via text, image and video input. "]}]]}]]}],["$","li","2",{"className":"mb-4xs last:mb-0","children":[["$","$2e",".$0",{"children":[["$","span","text-end-0",{"children":["For Sora, we developed a classifier to analyze text and images to predict whether an individual under the age of 18 is depicted or if the accompanying caption references a minor. We reject requests for image to video that contain under-18 individuals. If text-to-video is determined to be under 18, we enforce much stricter thresholds for moderation related to sexual, violent or self-harm content. "]}]]}]]}]]}],["$","p","4",{"className":"mb-sm last:mb-0","children":[["$","$2e","0",{"children":[["$","span","text-end-0",{"children":["Below is our evaluation for our under-18 classifier for humans. We evaluate our classifier for rejecting realistic under-18 individuals on a dataset containing close to 5000 images across the categories of [child | adult] and [realistic | fictitious]. Our policy stance is to reject realistic children, while allowing fictitious images including animated, cartoon, or sketch style, provided they are non-sexual. We have taken a cautious approach to content involving minors, and will continue to evaluate our approach as we learn more through product use and find the right balance between allowing for creative expression and safety. "]}]]}]]}],["$","p","5",{"className":"mb-sm last:mb-0","children":[["$","$2e","0",{"children":[["$","span","text-0",{"children":["Currently, our classifiers are highly accurate, but they may occasionally flag adult or non-realistic images of children by mistake. Additionally, we acknowledge that studies and existing literature highlight the potential for age prediction models to exhibit racial biases. For instance, these models may systematically underestimate the age of individuals from certain racial groups."]}],["$","sup","sup-563",{"children":["$","$L31","0",{"number":"5"}]}],["$","span","text-end-393",{"children":["  We are committed to enhancing the performance of our classifier, minimizing false positives, and deepening our understanding of potential biases over the coming months."]}]]}]]}]]}]}]]}]}],["$","div","XEk8TOlP4B5Rvs2dHgqI5",{"className":"","children":["$","$25",null,{"fallback":null,"children":[["$","$L26",null,{"moduleIds":["definitions/custom_components.ts -> @/custom_components/Sora/Table"]}],"$L3d"]}]}],["$","div","3TlIb5FGMZ4M7pQlNTXTSo",{"className":"","children":["$","$25",null,{"fallback":null,"children":[["$","$L26",null,{"moduleIds":"$30"}],["$","div",null,{"className":"w-full grid grid-cols-12 @container max-w-container multi-columns:px-0 multi-columns:flex","children":["$","div",null,{"className":"col-span-12 @md:col-span-6 @md:col-start-4 full-grid-content:@md:col-span-12 full-grid-content:@md:col-start-1 max-w-none prose","children":[["$","p","0",{"className":"mb-sm last:mb-0","children":[["$","b","0",{"children":[["$","span","text-end-0",{"children":["Output"]}]]}]]}],["$","p","1",{"className":"mb-sm last:mb-0","children":[["$","$2e","0",{"children":[["$","span","text-end-0",{"children":["As mentioned above, once we identify a reference to minors on text input with our under-18 classifier, we enforce strict thresholds for moderation related to sexual, violent or self harm content on output. Below are the two output classifiers we use to achieve this: "]}]]}]]}],["$","ul","2",{"className":"mb-md marker:text-inherit last:mb-0 list-disc pl-2xs mx-3xs","children":[["$","li","0",{"className":"mb-4xs last:mb-0","children":[["$","$2e",".$0",{"children":[["$","span","text-end-0",{"children":["Multi modal moderation classifier which scans for unsafe video outputs reject requests that may be particularly sensitive "]}]]}]]}],["$","li","1",{"className":"mb-4xs last:mb-0","children":[["$","$2e",".$0",{"children":[["$","span","text-end-0",{"children":["We are also leveraging our existing DALL·E image classifier to scan for violations related to child safety."]}]]}]]}]]}],["$","p","3",{"className":"mb-sm last:mb-0","children":[["$","$2e","0",{"children":[["$","span","text-end-0",{"children":["Our output classifiers scan 2 frames per second and when determining a video as unsafe we block any output."]}]]}]]}],["$","p","4",{"className":"mb-sm last:mb-0","children":[["$","$2e","0",{"children":[["$","span","text-end-0",{"children":["In addition to our classifiers and automated moderation, we will have human review as an additional layer of protection against potential child safety violations. "]}]]}]]}],["$","p","5",{"className":"mb-sm last:mb-0","children":[["$","b","0",{"children":[["$","span","text-end-0",{"children":["Product Policy"]}]]}]]}],["$","p","6",{"className":"mb-sm last:mb-0","children":[["$","$2e","0",{"children":[["$","span","text-end-0",{"children":["Our policies prohibit the use of Sora for the generation of sexual content involving minors. Violations of our child safety policies can result in content removal and banning of the user."]}]]}]]}]]}]}]]}]}],["$","div","4FM0wZVWDD6BnoAHj3RV6J",{"className":"@md:mt-[3.5rem] mt-[2rem] @md:-mb-[2.5rem] -mb-[1.75rem]","children":["$","$25",null,{"fallback":null,"children":[["$","$L26",null,{"moduleIds":"$2c"}],"$L3e"]}]}],["$","div","6WsRNLUP6IhqnbUyE41bdT",{"className":"","children":["$","$25",null,{"fallback":null,"children":[["$","$L26",null,{"moduleIds":"$30"}],["$","div",null,{"className":"w-full grid grid-cols-12 @container max-w-container multi-columns:px-0 multi-columns:flex","children":["$","div",null,{"className":"col-span-12 @md:col-span-6 @md:col-start-4 full-grid-content:@md:col-span-12 full-grid-content:@md:col-start-1 max-w-none prose","children":[["$","p","0",{"className":"mb-sm last:mb-0","children":[["$","$2e","0",{"children":[["$","span","text-end-0",{"children":["One of the emerging risk areas associated with AI video generation capabilities is the potential creation of NSFW (Not Safe for Work) or NCII (Non-Consensual Intimate Imagery) content. 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