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arxiv 1812.08685 v1 pith:GE2QCEGI submitted 2018-12-20 cs.CV

DeepFakes: a New Threat to Face Recognition? Assessment and Detection

Pavel Korshunov , Sebastien Marcel This is my paper
classification cs.CV
keywords videosdeepfakefacedetectionqualitydeepfakesmethodsrecognition
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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It is becoming increasingly easy to automatically replace a face of one person in a video with the face of another person by using a pre-trained generative adversarial network (GAN). Recent public scandals, e.g., the faces of celebrities being swapped onto pornographic videos, call for automated ways to detect these Deepfake videos. To help developing such methods, in this paper, we present the first publicly available set of Deepfake videos generated from videos of VidTIMIT database. We used open source software based on GANs to create the Deepfakes, and we emphasize that training and blending parameters can significantly impact the quality of the resulted videos. To demonstrate this impact, we generated videos with low and high visual quality (320 videos each) using differently tuned parameter sets. We showed that the state of the art face recognition systems based on VGG and Facenet neural networks are vulnerable to Deepfake videos, with 85.62% and 95.00% false acceptance rates respectively, which means methods for detecting Deepfake videos are necessary. By considering several baseline approaches, we found that audio-visual approach based on lip-sync inconsistency detection was not able to distinguish Deepfake videos. The best performing method, which is based on visual quality metrics and is often used in presentation attack detection domain, resulted in 8.97% equal error rate on high quality Deepfakes. Our experiments demonstrate that GAN-generated Deepfake videos are challenging for both face recognition systems and existing detection methods, and the further development of face swapping technology will make it even more so.

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Cited by 8 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. The DeepFake Detection Challenge (DFDC) Dataset

    cs.CV 2020-06 accept novelty 7.0

    The DFDC dataset is the largest public collection of face-swapped videos and supports detectors that generalize to in-the-wild deepfakes.

  2. SynthForensics: Benchmarking and Evaluating People-Centric Synthetic Video Deepfakes

    cs.CV 2026-02 unverdicted novelty 6.0

    SynthForensics is a people-centric benchmark where face-based detectors lose 13-55 AUC points on modern synthetic videos compared to legacy manipulation sets.

  3. Orthogonal Subspace Decomposition for Generalizable AI-Generated Image Detection

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    Orthogonal subspace decomposition via SVD on vision foundation model features preserves high-rank pre-trained knowledge by freezing principal components and adapting residuals, reducing overfitting for better generali...

  4. We Need No Pixels: Video Manipulation Detection Using Stream Descriptors

    cs.LG 2019-06 unverdicted novelty 6.0

    Video forgeries are detectable via binary classification on multimedia stream descriptors without pixel analysis.

  5. VendorBench-100: A Unified Cross-Paradigm Benchmark for Deepfake Image Detection

    cs.CV 2026-07 conditional novelty 5.0

    A 100-image cross-paradigm benchmark of 36 deepfake detectors reveals that ROC-AUC and MCC diverge sharply, meaning strong class-separation ranking does not guarantee reliable default-threshold decisions.

  6. Towards High Fidelity Face Swapping: A Comprehensive Survey and New Benchmark

    cs.CV 2026-04 unverdicted novelty 5.0

    Organizes existing face swapping techniques into five paradigms, releases the CASIA FaceSwapping benchmark with demographic balance, and runs experiments under new standardized protocols to reveal performance patterns.

  7. Towards trustworthy management of AIGC copyright: blockchain-enabled full lifecycle recording and multi-party auditing approach

    cs.CY 2024-06 unverdicted novelty 4.0

    AIGC-Chain records the full lifecycle of AI-generated content on blockchain to support multi-party auditing and copyright ownership determination in disputes.

  8. The Mass, Fake News, and Cognition Security

    cs.CY 2019-07 unverdicted novelty 3.0

    The paper defines Cognition Security (CogSec) as a multidisciplinary field studying cognitive impacts of fake news and outlines research challenges, techniques, and future directions.