The Presidential Deepfakes Dataset

How do we evaluate media forensic techniques for detecting deepfakes? We present the Presidential Deepfakes Dataset (PDD), which consists of 32 videos, half of which are original videos and half of which are manipulated with audio impersonations, synthesized lip synchronizations, political misinformation, and situational artifacts. This dataset expands the context on which end-to-end media forensic systems can be evaluated. As an example, we evaluate the winning model of the DeepFake Detection Challenge on the PDD and find that it classifies 69% of the videos in the PDD accurately. We share this dataset publicly for researchers to evaluate their techniques with the intention of pre-bunking future misinformation attempts.

[1]  David G. Rand,et al.  Shifting attention to accuracy can reduce misinformation online , 2021, Nature.

[2]  C. V. Jawahar,et al.  A Lip Sync Expert Is All You Need for Speech to Lip Generation In the Wild , 2020, ACM Multimedia.

[3]  Gianluca Demartini,et al.  The COVID-19 Infodemic: Can the Crowd Judge Recent Misinformation Objectively? , 2020, CIKM.

[4]  Cristian Canton Ferrer,et al.  The DeepFake Detection Challenge (DFDC) Dataset. , 2020 .

[5]  Ser-Nam Lim,et al.  Detecting Deep-Fake Videos from Appearance and Behavior , 2020, 2020 IEEE International Workshop on Information Forensics and Security (WIFS).

[6]  Siwei Lyu,et al.  Deepfake Detection: Current Challenges and Next Steps , 2020, 2020 IEEE International Conference on Multimedia & Expo Workshops (ICMEW).

[7]  Chen Change Loy,et al.  DeeperForensics-1.0: A Large-Scale Dataset for Real-World Face Forgery Detection , 2020, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[8]  Siwei Lyu,et al.  Celeb-DF: A Large-Scale Challenging Dataset for DeepFake Forensics , 2019, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[9]  Iyad Rahwan,et al.  Human detection of machine-manipulated media , 2019, Commun. ACM.

[10]  Andreas Rössler,et al.  FaceForensics++: Learning to Detect Manipulated Facial Images , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).

[11]  Sébastien Marcel,et al.  DeepFakes: a New Threat to Face Recognition? Assessment and Detection , 2018, ArXiv.

[12]  Xin Yang,et al.  Exposing Deep Fakes Using Inconsistent Head Poses , 2018, ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[13]  Sinan Aral,et al.  The spread of true and false news online , 2018, Science.

[14]  Ira Kemelmacher-Shlizerman,et al.  Synthesizing Obama , 2017, ACM Trans. Graph..

[15]  Joon Son Chung,et al.  Lip Reading Sentences in the Wild , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[16]  Rosalind W. Picard,et al.  Comparing Human and Machine Deepfake Detection with Affective and Holistic Processing , 2021, ArXiv.

[17]  H. Farid,et al.  Protecting World Leaders Against Deep Fakes , 2019, CVPR Workshops.