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It was also discovered that traditional artificial neural networks are no longer effective and require ...", "cleaned_text": null, "cleaned_text_custom_parser": null, "cleaned_text_custom_parser_2": null, "best_field": null, "enhanced": false, "date_published": null, "image": null, "num_tokens_oai_tokenizer": null, "num_tokens_gemini_tokenizer": null}, "favicon": null, "state": "candidate", "enhanced": false, "answer": null, "valid_answer": false, "source_citation_index": 4, "user_web_page_id": null, "status": "not_processed", "reader": false, "rank": 1, "reason": "The source provides a detailed survey on the effectiveness of deepfake detection techniques, directly supporting technical deep dives."}, {"id": 571658, "type": "GenericWebPage", "mime_type": "text/html", "url": "https://www.sciencedirect.com/science/article/abs/pii/S1566253525000661", "slug": "sciencedirect", "title": "Advances in DeepFake detection algorithms: Exploring fusion techniques in single and multi-modal approach - ScienceDirect", "snippet": "The rise in digitally altered images has made research on robust solutions for real image verification across sectors, including media and cybersecurity very essential. Deepfake technology\u2019s development compromises digital media\u2019s validity and calls for advanced detection to address.", "image": null, "date_published": null, "media": [], "products": [], "source": {"id": 571658, "website_id": 214, "url": "https://www.sciencedirect.com/science/article/abs/pii/S1566253525000661", "title": "Advances in DeepFake detection algorithms: Exploring fusion techniques in single and multi-modal approach - ScienceDirect", "snippet": "The rise in digitally altered images has made research on robust solutions for real image verification across sectors, including media and cybersecurity very essential. Deepfake technology\u2019s development compromises digital media\u2019s validity and calls for advanced detection to address.", "cleaned_text": null, "cleaned_text_custom_parser": null, "cleaned_text_custom_parser_2": null, "best_field": null, "enhanced": false, "date_published": null, "image": null, "num_tokens_oai_tokenizer": null, "num_tokens_gemini_tokenizer": null}, "favicon": "https://sdfestaticassets-us-east-1.sciencedirectassets.com/shared-assets/103/images/favSD.ico", "state": "candidate", "enhanced": false, "answer": null, "valid_answer": false, "source_citation_index": 5, "user_web_page_id": null, "status": "not_processed", "reader": false, "rank": 1, "reason": "This source discusses advancements in deepfake detection algorithms, focusing on emerging technologies relevant to the query."}, {"id": 523134, "type": "GenericWebPage", "mime_type": "text/html", "url": "https://wires.onlinelibrary.wiley.com/doi/10.1002/widm.1520", "slug": "wiley", "title": "Deepfake detection using deep learning methods: A systematic and comprehensive review - Heidari - 2024 - WIREs Data Mining and Knowledge Discovery - Wiley Online Library", "snippet": "It might result in various issues ... such considerations, it is critical to have technologies that can assist us in discerning reality. This study gives a complete assessment of the literature on deepfake detection strategies using DL-based algorithms....", "image": null, "date_published": null, "media": [], "products": [], "source": {"id": 523134, "website_id": 16569, "url": "https://wires.onlinelibrary.wiley.com/doi/10.1002/widm.1520", "title": "Deepfake detection using deep learning methods: A systematic and comprehensive review - Heidari - 2024 - WIREs Data Mining and Knowledge Discovery - Wiley Online Library", "snippet": "It might result in various issues ... such considerations, it is critical to have technologies that can assist us in discerning reality. 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Received 2025 Jan 16; Revised 2025 Feb 19; Accepted 2025 Feb 24; Collection date 2025 Mar.", "image": null, "date_published": null, "media": [], "products": [], "source": {"id": 571629, "website_id": 34203, "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC11943306/", "title": "Deepfake Media Forensics: Status and Future Challenges - PMC", "snippet": "Domenico Vitulano: Conceptualization, Methodology, Formal analysis, Investigation, Writing \u2013 original draft, Writing \u2013 review & editing, Visualization, Supervision ... 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In addition, we explore novel detection methods that utilize sophisticated machine learning, computer vision, and audio analysis techniques.", "image": null, "date_published": null, "media": [], "products": [], "source": {"id": 523125, "website_id": 214, "url": "https://www.sciencedirect.com/science/article/pii/S240584402500653X", "title": "Exploring autonomous methods for deepfake detection: A detailed survey on techniques and evaluation - ScienceDirect", "snippet": "We explore the fundamental technologies, such as deep learning models, and evaluate their efficacy in differentiating real and manipulated media. 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The winners of the Kaggle Competition were awarded $1,000,000. \u00b7 Rather than fine-tune the best machine learning model for this Kaggle competition, we are curious about strategies and techniques ...", "image": null, "date_published": null, "media": [], "products": [], "source": {"id": 523118, "website_id": 61982, "url": "https://www.media.mit.edu/projects/detect-fakes/overview/", "title": "Project Overview \u2039 Detect DeepFakes: How to counteract misinformation created by AI \u2013", "snippet": "The goal of the challenge is to ... new technologies that can help detect deepfakes and manipulated media." The winners of the Kaggle Competition were awarded $1,000,000. \u00b7 Rather than fine-tune the best machine learning model for this Kaggle competition, we are curious about strategies and techniques ...", "cleaned_text": "See for yourself how accurately you can identify AI-generated images at the DetectFakes Experiment and if you want to learn to spot deepfakes, please check out our recent paper on How to Distinguish AI-Generated Images from Authentic Photographs\n\nYou can find more of our work at publications in PNAS, a workshop at IJCAI, and pre-print on arXiv.\n\nCheck out a video from the Election Misinformation Symposium: Fighting Misinfo Through Fact-checking and Deepfake Detection\n\nFind our deepfake research discussed in the news: Science, Scientific American, BBC, WSJ, NYT, , and NPR\n\nNow, here's an excerpt from a couple years ago:\n\nHow do you spot a DeepFake? How good are DeepFake videos? How well can ordinary people tell the difference between a video manipulated by AI and a normal, non-altered video? Rather than try to explain in words, we built the Detect Fakes website so you can see the answer for yourself. Detect Fakes is a research project designed to answer these questions and identify techniques to counteract AI-generated misinformation. It turns out there are many subtle signs that a video has been algorithmically manipulated. Some subtleties are explained in detail below.", "cleaned_text_custom_parser": "Work for a Member company and need a Member Portal account? Register here with your company email address.\nDetect DeepFakes: How to counteract misinformation created by AI\nIn this Project:\nProject Contact:\nGroups\nSee for yourself how accurately you can identify AI-generated images at theDetectFakes Experimentand if you want to learn to spot deepfakes, please check out our recent paper onHow to Distinguish AI-Generated Images from Authentic Photographs\nYou can find more of our work at publications inPNAS, a workshop atIJCAI, andpre-printon arXiv.\nCheck out a video from theElection Misinformation Symposium: Fighting Misinfo Through Fact-checking and Deepfake Detection\nFind our deepfake research discussed in the news:Science,Scientific American,BBC,WSJ,NYT, , andNPR\nHow do you spot a DeepFake? How good are DeepFake videos? How well can ordinary people tell the difference between a video manipulated by AI and a normal, non-altered video? Rather than try to explain in words, we built theDetect Fakeswebsite so you can see the answer for yourself. Detect Fakes is a research project designed to answer these questions and identify techniques to counteract AI-generated misinformation.\u00a0It turns out there are many subtle signs that a video has been algorithmically manipulated. Some subtleties are explained in detail below.\nThe airbrush effect on the left is an example of an artificial intelligence manipulation\nWe already know DeepFakes can be quite believable, but just how believable are they?Kaggle's Deepfake Detection Challenge (DFDC)recently sought an algorithmic answer to this question of detecting fakes. The description on the Kaggle Website explains, \"AWS, Facebook, Microsoft, the Partnership on AI\u2019s Media Integrity Steering Committee, and academics have come together to build the Deepfake Detection Challenge (DFDC). The goal of the challenge is to spur researchers around the world to build innovative new technologies that can help detect deepfakes and manipulated media.\" The winners of the Kaggle Competition were awarded $1,000,000.\nRather than fine-tune the best machine learning model for this Kaggle competition, we are curious about strategies and techniques for building public awareness of DeepFake technology and\u00a0 helping ordinary people think critically about the media that they consume.\nWe hypothesized that the exposure of how DeepFakes look and the experience of detecting subtle computational manipulations will increase people's ability to discern a wide-range of video manipulations in the future. As such, we hosted a website calledDetect Fakesto display thousands of the curated, high-quality DeepFake and real videos from the DFDC dataset publicly.\u00a0 The latest version of the website shows 32 videos that were produced as part of the Presidential Deepfakes Dataset.\nThe Detect Fakes experiment offers the opportunity to learn more about DeepFakes and see how well you can discern real from fake. When it comes to AI-manipulated media, there's no single tell-tale sign of how to spot a fake. Nonetheless, there are several DeepFake artifacts that you can be on the look out for.\nThese eight questions are intended to help guide people looking through DeepFakes. High-quality DeepFakes are not easy to discern, but with practice, people can build intuition for identifying what is fake and what is real. You can practice trying to detect DeepFakes atDetect Fakes.\nResearch Topics\nMore ways to explore", "cleaned_text_custom_parser_2": "\n\n \n

\n \n

\n Login\n
\n Register\n

\n \n

\n Work for a Member company and need a Member Portal account? Register here with your company email address.\n

\n \n

\n Project\n

\n Detect DeepFakes: How to counteract misinformation created by AI\n

\n Creative Commons\n

\n \n Attribution 4.0 International\n \n

\n Matt\n

\n Project Contact:\n

\n Matt Groh\n
\n \n matthew.groh@kellogg.northwestern.edu\n \n
\n \n Project Website\n \n
\n \n Other Press Inquiries\n \n

\n Groups\n

\n Detect DeepFakes: How to counteract misinformation created by AI was active from April 2020 to January 2025\n

\n See for yourself how accurately you can identify AI-generated images at the\n \n DetectFakes Experiment\n \n and if you want to learn to spot deepfakes, please check out our recent paper on\n \n How to Distinguish AI-Generated Images from Authentic Photographs\n \n

\n You can find more of our work at publications in\n \n PNAS\n \n , a workshop at\n \n IJCAI\n \n , and\n \n pre-print\n \n on arXiv.\n

\n Check out a video from the\n \n Election Misinformation Symposium: Fighting Misinfo Through Fact-checking and Deepfake Detection\n \n

\n Find our deepfake research discussed in the news:\n \n Science\n \n ,\n \n Scientific American\n \n ,\n \n BBC\n \n ,\n \n WSJ\n \n ,\n \n NYT\n \n , , and\n \n NPR\n \n

\n Now, here's an excerpt from a couple years ago:\n

\n How do you spot a DeepFake? How good are DeepFake videos? How well can ordinary people tell the difference between a video manipulated by AI and a normal, non-altered video? Rather than try to explain in words, we built the\n \n Detect Fakes\n \n website so you can see the answer for yourself. Detect Fakes is a research project designed to answer these questions and identify techniques to counteract AI-generated misinformation.\u00a0It turns out there are many subtle signs that a video has been algorithmically manipulated. Some subtleties are explained in detail below.\n

\n \n Read the latest paper on this research\n \n

\n Creative Commons\n

\n \n Attribution 4.0 International\n \n

\n We already know DeepFakes can be quite believable, but just how believable are they?\n \n Kaggle's Deepfake Detection Challenge (DFDC)\n \n recently sought an algorithmic answer to this question of detecting fakes. The description on the Kaggle Website explains, \"AWS, Facebook, Microsoft, the Partnership on AI\u2019s Media Integrity Steering Committee, and academics have come together to build the Deepfake Detection Challenge (DFDC). The goal of the challenge is to spur researchers around the world to build innovative new technologies that can help detect deepfakes and manipulated media.\" The winners of the Kaggle Competition were awarded $1,000,000.\n

\n Rather than fine-tune the best machine learning model for this Kaggle competition, we are curious about strategies and techniques for building public awareness of DeepFake technology and\u00a0 helping ordinary people think critically about the media that they consume.\n

\n We hypothesized that the exposure of how DeepFakes look and the experience of detecting subtle computational manipulations will increase people's ability to discern a wide-range of video manipulations in the future. As such, we hosted a website called\n \n Detect Fakes\n \n to display thousands of the curated, high-quality DeepFake and real videos from the DFDC dataset publicly.\u00a0 The latest version of the website shows 32 videos that were produced as part of the Presidential Deepfakes Dataset.\n

\n The Detect Fakes experiment offers the opportunity to learn more about DeepFakes and see how well you can discern real from fake. When it comes to AI-manipulated media, there's no single tell-tale sign of how to spot a fake. Nonetheless, there are several DeepFake artifacts that you can be on the look out for.\n

\n Pay attention to the face. High-end DeepFake manipulations are almost always facial transformations.\n
\n Pay attention to the cheeks and forehead. Does the skin appear too smooth or too wrinkly? Is the agedness of the skin similar to the agedness of the hair and eyes? DeepFakes may be incongruent on some dimensions.\n
\n Pay attention to the eyes and eyebrows. Do shadows appear in places that you would expect? DeepFakes may fail to fully represent the natural physics of a scene.\n
\n Pay attention to the glasses. Is there any glare? Is there too much glare? Does the angle of the glare change when the person moves? Once again, DeepFakes may fail to fully represent the natural physics of lighting.\n
\n Pay attention to the facial hair or lack thereof. Does this facial hair look real? DeepFakes might add or remove a mustache, sideburns, or beard. But, DeepFakes may fail to make facial hair transformations fully natural.\n
\n Pay attention to facial moles.\u00a0 Does the mole look real?\n
\n Pay attention to blinking. Does the person blink enough or too much?\n
\n Pay attention to the lip movements. Some deepfakes are based on lip syncing. Do the lip movements look natural?\n

\n These eight questions are intended to help guide people looking through DeepFakes. High-quality DeepFakes are not easy to discern, but with practice, people can build intuition for identifying what is fake and what is real. You can practice trying to detect DeepFakes at\n \n Detect Fakes\n \n .\n

\n Research Topics\n

\n \n #human-computer interaction\n \n \n #artificial intelligence\n \n \n #data\n \n \n #government\n \n \n #human-machine interaction\n \n \n #politics\n \n \n #social media\n \n \n #social science\n \n \n #machine learning\n \n \n #ethics\n \n \n #computer science\n \n \n #technology\n \n

\n \n\n", "best_field": "cleaned_text", "enhanced": true, "date_published": null, "image": null, "num_tokens_oai_tokenizer": null, "num_tokens_gemini_tokenizer": null}, "favicon": "https://www.media.mit.edu/static/images/icons/apple-icon-57x57.1737f411eb73.png", "state": "candidate", "enhanced": true, "answer": null, "valid_answer": false, "source_citation_index": 10, "user_web_page_id": null, "status": "ready", "reader": false, "rank": 2, "reason": "The source discusses the development of technologies to counter deepfake misinformation, relevant to the query's technology focus."}]

Fast facts: AI-generated image detection

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