Deepfake Detection: A Systematic Literature Review | IEEE Journals & Magazine | IEEE Xplore

A Comprehensive Review of Deepfake Detection Techniques: Challenges, Methodologies, and Future Directions | Journal of Neonatal Surgery

[2308.05563] Recent Advancements In The Field Of Deepfake Detection

Full article: A systematic literature review on the effectiveness of deepfake detection techniques

Advances in DeepFake detection algorithms: Exploring fusion techniques in single and multi-modal approach - ScienceDirect

Deepfake detection using deep learning methods: A systematic and comprehensive review - Heidari - 2024 - WIREs Data Mining and Knowledge Discovery - Wiley Online Library

Deepfake Media Forensics: Status and Future Challenges - PMC

Exploring autonomous methods for deepfake detection: A detailed survey on techniques and evaluation - ScienceDirect

Distinguishing Reality from AI: Approaches for Detecting Synthetic Content

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

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

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

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

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

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 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.

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Detect DeepFakes: How to counteract misinformation created by AI
In this Project:
Project Contact:
Groups
See 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
You can find more of our work at publications inPNAS, a workshop atIJCAI, andpre-printon arXiv.
Check out a video from theElection Misinformation Symposium: Fighting Misinfo Through Fact-checking and Deepfake Detection
Find our deepfake research discussed in the news:Science,Scientific American,BBC,WSJ,NYT, , andNPR
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 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. It turns out there are many subtle signs that a video has been algorithmically manipulated. Some subtleties are explained in detail below.
The airbrush effect on the left is an example of an artificial intelligence manipulation
We 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’s 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.
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  helping ordinary people think critically about the media that they consume.
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 calledDetect Fakesto display thousands of the curated, high-quality DeepFake and real videos from the DFDC dataset publicly.  The latest version of the website shows 32 videos that were produced as part of the Presidential Deepfakes Dataset.
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.
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 atDetect Fakes.
Research Topics
More ways to explore

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      Detect DeepFakes: How to counteract misinformation created by AI
     </h1>
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        Creative Commons
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       Detect DeepFakes: How to counteract misinformation created by AI was active from April 2020 to January 2025
       <div>
        <p>
         See for yourself how accurately you can identify AI-generated images at the
         <a href="https://detectfakes.kellogg.northwestern.edu/">
          DetectFakes Experiment
         </a>
         and if you want to learn to spot deepfakes, please check out our recent paper on
         <a href="https://arxiv.org/abs/2406.08651">
          How to Distinguish AI-Generated Images from Authentic Photographs
         </a>
        </p>
        <p>
         You can find more of our work at publications in
         <a href="https://www.pnas.org/doi/10.1073/pnas.2110013119">
          PNAS
         </a>
         , a workshop at
         <a href="http://ceur-ws.org/Vol-2942/paper3.pdf">
          IJCAI
         </a>
         , and
         <a href="https://arxiv.org/abs/2202.12883">
          pre-print
         </a>
         on arXiv.
        </p>
        <p>
         Check out a video from the
         <a href="https://youtu.be/QlNGD_QLcZE">
          Election Misinformation Symposium: Fighting Misinfo Through Fact-checking and Deepfake Detection
         </a>
        </p>
        <p>
         Find our deepfake research discussed in the news:
         <a href="https://www.science.org/content/article/how-spot-deepfake-and-prevent-it-causing-political-chaos">
          Science
         </a>
         ,
         <a href="https://open.spotify.com/episode/0lblecG4mUDznD2mMUim5S">
          Scientific American
         </a>
         ,
         <a href="https://www.bbc.co.uk/sounds/play/w3ct4vc0">
          BBC
         </a>
         ,
         <a href="https://www.wsj.com/articles/is-it-human-or-ai-new-tools-help-you-spot-the-bots-11673356404">
          WSJ
         </a>
         ,
         <a href="https://www.nytimes.com/2018/10/22/business/efforts-to-acknowledge-the-risks-of-new-ai-technology.html">
          NYT
         </a>
         , , and
         <a href="https://open.spotify.com/episode/0lWz7XmciHMD7yuKKXzplT?go=1&amp;sp_cid=8c06ef053e45210d99bedc25282ba490&amp;utm_source=embed_player_p&amp;utm_medium=desktop&amp;nd=1">
          NPR
         </a>
        </p>
        <p>
         Now, here's an excerpt from a couple years ago:
        </p>
        <p>
         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
         <a href="http://detectfakes.media.mit.edu/">
          Detect Fakes
         </a>
         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.
        </p>
       </div>
      </div>
      <div>
       <a href="https://www.media.mit.edu/publications/human-detection-of-political-deepfakes-across-transcripts-audio-and-video/">
        Read the latest paper on this research
       </a>
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         <img src="https://dam-prod2.media.mit.edu/thumb/2020/04/14/Screen Shot 2020-04-14 at 11.08.01 AM.png.1400x1400.png"/>
         <div>
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           Creative Commons
          </p>
          <p>
           <a href="https://creativecommons.org/licenses/by/4.0/">
            Attribution 4.0 International
           </a>
          </p>
         </div>
        </div>
       </fieldset>
       <div>
        <p>
         We already know DeepFakes can be quite believable, but just how believable are they?
         <a href="https://www.kaggle.com/c/deepfake-detection-challenge/overview">
          Kaggle's Deepfake Detection Challenge (DFDC)
         </a>
         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’s 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.
        </p>
        <p>
         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  helping ordinary people think critically about the media that they consume.
        </p>
        <p>
         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
         <a href="https://detectfakes.media.mit.edu/">
          Detect Fakes
         </a>
         to display thousands of the curated, high-quality DeepFake and real videos from the DFDC dataset publicly.  The latest version of the website shows 32 videos that were produced as part of the Presidential Deepfakes Dataset.
        </p>
        <p>
         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.
        </p>
        <ol>
         <li>
          Pay attention to the face. High-end DeepFake manipulations are almost always facial transformations.
         </li>
         <li>
          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.
         </li>
         <li>
          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.
         </li>
         <li>
          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.
         </li>
         <li>
          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.
         </li>
         <li>
          Pay attention to facial moles.  Does the mole look real?
         </li>
         <li>
          Pay attention to blinking. Does the person blink enough or too much?
         </li>
         <li>
          Pay attention to the lip movements. Some deepfakes are based on lip syncing. Do the lip movements look natural?
         </li>
        </ol>
        <p>
         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
         <a href="https://detectfakes.media.mit.edu/">
          Detect Fakes
         </a>
         .
        </p>
       </div>
      </div>
      <div>
       <h5>
        Research Topics
       </h5>
       <a href="https://www.media.mit.edu/research/?filter=everything&amp;tag=human-computer-interaction">
        #human-computer interaction
       </a>
       <a href="https://www.media.mit.edu/research/?filter=everything&amp;tag=artificial-intelligence">
        #artificial intelligence
       </a>
       <a href="https://www.media.mit.edu/research/?filter=everything&amp;tag=data">
        #data
       </a>
       <a href="https://www.media.mit.edu/research/?filter=everything&amp;tag=government">
        #government
       </a>
       <a href="https://www.media.mit.edu/research/?filter=everything&amp;tag=human-machine-interaction">
        #human-machine interaction
       </a>
       <a href="https://www.media.mit.edu/research/?filter=everything&amp;tag=politics">
        #politics
       </a>
       <a href="https://www.media.mit.edu/research/?filter=everything&amp;tag=social-media">
        #social media
       </a>
       <a href="https://www.media.mit.edu/research/?filter=everything&amp;tag=social-science">
        #social science
       </a>
       <a href="https://www.media.mit.edu/research/?filter=everything&amp;tag=machine-learning">
        #machine learning
       </a>
       <a href="https://www.media.mit.edu/research/?filter=everything&amp;tag=ethics">
        #ethics
       </a>
       <a href="https://www.media.mit.edu/research/?filter=everything&amp;tag=computer-science">
        #computer science
       </a>
       <a href="https://www.media.mit.edu/research/?filter=everything&amp;tag=technology">
        #technology
       </a>
      </div>
     </div>
    </div>
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Project Overview ‹ Detect DeepFakes: How to counteract misinformation created by AI –

Deepfake Detection: A Systematic Literature Review | IEEE Journals & Magazine | IEEE Xplore
https://ieeexplore.ieee.org/document/9721302/
A Comprehensive Review of Deepfake Detection Techniques: Challenges, Methodologies, and Future Directions | Journal of Neonatal Surgery
https://www.jneonatalsurg.com/index.php/jns/article/view/4926
[2308.05563] Recent Advancements In The Field Of Deepfake Detection
https://arxiv.org/abs/2308.05563
Full article: A systematic literature review on the effectiveness of deepfake detection techniques
https://www.tandfonline.com/doi/full/10.1080/23742917.2023.2192888
Advances in DeepFake detection algorithms: Exploring fusion techniques in single and multi-modal approach - ScienceDirect
https://www.sciencedirect.com/science/article/abs/pii/S1566253525000661
Deepfake detection using deep learning methods: A systematic and comprehensive review - Heidari - 2024 - WIREs Data Mining and Knowledge Discovery - Wiley Online Library
https://wires.onlinelibrary.wiley.com/doi/10.1002/widm.1520
Deepfake Media Forensics: Status and Future Challenges - PMC
https://pmc.ncbi.nlm.nih.gov/articles/PMC11943306/
Exploring autonomous methods for deepfake detection: A detailed survey on techniques and evaluation - ScienceDirect
https://www.sciencedirect.com/science/article/pii/S240584402500653X
Distinguishing Reality from AI: Approaches for Detecting Synthetic Content
https://www.mdpi.com/2073-431X/14/1/1
Project Overview ‹ Detect DeepFakes: How to counteract misinformation created by AI –
https://www.media.mit.edu/projects/detect-fakes/overview/

Peer-reviewed deepfake detection papers

Deepfake Detection: A Systematic Literature Review | IEEE Journals & Magazi...

A Comprehensive Review of Deepfake Detection Techniques: Challenges, Method...

[2308.05563] Recent Advancements In The Field Of Deepfake Detection

Full article: A systematic literature review on the effectiveness of deepfa...

Advances in DeepFake detection algorithms: Exploring fusion techniques in s...

Deepfake detection using deep learning methods: A systematic and comprehens...

Deepfake Media Forensics: Status and Future Challenges - PMC

Exploring autonomous methods for deepfake detection: A detailed survey on t...

Distinguishing Reality from AI: Approaches for Detecting Synthetic Content

Project Overview ‹ Detect DeepFakes: How to counteract misinformation creat...

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