Evading DeepFake Detectors via Adversarial Statistical Consistency
Open accessAbstract
In recent years, as various realistic face forgery techniques known as DeepFake improves by leaps and bounds, more and more DeepFake detection techniques have been proposed. These methods typically rely on detecting statistical differences between natural (i.e., real) and DeepFake-generated images in both spatial and frequency domains. In this work, we propose to explicitly minimize the statistical differences to evade state-of-the-art DeepFake detectors. To this end, we propose a statistical consistency attack (StatAttack) against DeepFake detectors, which contains two main parts. First, we select several statistical-sensitive natural degradations (i.e., exposure, blur, and noise) and add them to the fake images in an adversarial way. Second, we find that the statistical differences between natural and DeepFake images are positively associated with the distribution shifting between the two kinds of images, and we propose to use a distribution-aware loss to guide the optimization of different degradations. As a result, the feature distributions of generated adversarial examples is close to the natural images. Furthermore, we extend the StatAttack to a more powerful version, MStatAttack, where we extend the single-layer degradation to multi-layer degradations sequentially and use the loss to tune the combination weights jointly. Comprehensive experimental results on four spatial-based detectors and two frequency-based detectors with four datasets demonstrate the effectiveness of our proposed attack method in both white-box and black-box settings.
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None of the existing datasets incorporate adversarially perturbed deepfakes designed to evade detection.
“None of the existing datasets incorporate adversarially perturbed DFs designed to evade detection, which is a critical real-world threat [17, 21].”
From Deepfake detection across image, video, and audio: a comprehensive survey with empirical evaluation of generalization and robustness (2026) · cites this paper as [21]
The Introduction says transferable adversarial examples pose a practical threat to DeepFake detection. Section 6.1 describes the datasets used in the experiments, but neither it nor the other passages establishes that no existing dataset contains adversarially perturbed deepfakes. The categorical dataset claim is therefore unsupported by this paper. (LLM, conf=0.85)
Experimental — accuracy not yet measuredDF detectors are increasingly vulnerable to adversarial attacks, which introduce carefully crafted perturbations into DF content to deceive detection systems while remaining imperceptible to humans.
“DF detectors are increasingly vulnerable to adversarial attacks, which introduce carefully crafted perturbations into DF content to deceive detection systems while remaining imperceptible to humans.”
From Deepfake detection across image, video, and audio: a comprehensive survey with empirical evaluation of generalization and robustness (2026) · cites this paper as [21]
The Introduction says adversarial perturbations can bypass DeepFake detectors, and §6.4 reports that the paper's attacks maintain image quality and produce natural-looking examples. The copy does not establish that the perturbations are imperceptible to humans or that detector vulnerability is increasing over time. (LLM, conf=0.85)
Experimental — accuracy not yet measuredExisting surveys do not examine how detection methods withstand adversarial attacks designed to evade detection or postprocessing operations that degrade detection performance.
“Existing surveys do not examine how detection methods withstand adversarial attacks designed to evade detection [17, 21] or postprocessing operations that degrade detection performance [22-24].”
From Deepfake detection across image, video, and audio: a comprehensive survey with empirical evaluation of generalization and robustness (2026) · cites this paper as [21]
Recent studies have demonstrated that even SoTA detection models can be significantly compromised by adversarial examples.
“Recent studies have demonstrated that even SoTA detection models can be significantly compromised by adversarial examples (AEs) [21, 197-199].”
From Deepfake detection across image, video, and audio: a comprehensive survey with empirical evaluation of generalization and robustness (2026) · cites this paper as [21]
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A Novel Adversarial Gray-Box Attack on DCT-Based Face Deepfake Detectors
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Adversarial Reality for Evading Deepfake Image Detectors
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Where the Devil Hides: Deepfake Detectors Can No Longer Be Trusted
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Counterfactual Explanations for Face Forgery Detection via Adversarial Removal of Artifacts
Li, Y., Yang, S., Wang, W., He, Z., Peng, B., & Dong, J. (2024) · IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme57554.2024.10688373 · Cited by 3 · see citing works
Exploring Strengths and Weaknesses of Super-Resolution Attack in Deepfake Detection
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HybridNet: Advancing Deepfake Detection Through Residual, SE, and Depthwise Convolutions
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Detecting anti-forensic deepfakes with identity-aware multi-branch networks
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Optimal transport-guided framework for adversarial robust deepfake detection
Chatterjee, S. (2026) · Discover Computing · doi:10.1007/s10791-026-09904-2 · Cited by 2 · see citing works
A Systematic Review of Adversarial Machine Learning and Deep Learning Applications
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Selective Consistency Gradient Attack: Resolving Multi-Target Gradient Conflicts in Object Detection
Huang, D., Jia, T., Zhang, P., Ji, R., & Ge, S. S. (2025) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49660.2025.10888709 · Cited by 1 · see citing works
Survey on generative visual media detection and security
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Adversarial Diffusion Model: Generating High-Quality and Undetectable Images From Scratch
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Unsupervised Domain Adaptation-Based Cross-Type Deepfake Image Detection
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Deepfake Detection Techniques and Analysis
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Evading DeepFake Detectors via Conditional Diffusion Models
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Adversarial Magnification to Deceive Deepfake Detection Through Super Resolution
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Exploring Feature Map Correlations for Effective Fake Face Detection
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Deepfake Face Provenance for Proactive Forensics
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HOG Landmarks and Illumination Artifacts for Deep Fake Detection
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Comparative Study: Deepfake Detection in Tamil Speech using Hybrid Convolutional Networks
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ADPF: Adversarial Sample Detection Based on Prediction Feature
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SwinGAN: A Synergistic Approach Combining Swin Transformers and GANs for DeepFake Detection
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Take Fake as Real: Realistic-Like Robust Black-Box Adversarial Attack to Evade AIGC Detection
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- Authors
- Hou, Y., Guo, Q., Huang, Y., Xie, X., Ma, L., & Zhao, J.
- Affiliations
- Kyushu University (JP)
- Agency for Science, Technology and Research (SG)
- Nanyang Technological University (SG)
- Singapore Management University (SG)
- University of Alberta (CA)
- Venue
- 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2023-06
- Pages
- 12271-12280
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W4386071537
- Cited by
- 76
- References
- 51
- Citations by year
- 2026: 16 · 2025: 31 · 2024: 25 · 2023: 4
- FWCI
- 8.13
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- Singapore Management University Institutional Knowledge (InK) (Singapore Management University) (submittedVersion, other-oa)
- arXiv (Cornell University)
- Funders
- Canadian Institute for Advanced Research
- Natural Sciences and Engineering Research Council of Canada
- Japan Society for the Promotion of Science (JPMJSP2136, 20H04168, 21H04877)
- JST-Mirai Program (JPMJSP 2136, JPMJMI20B8)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Digital Media Forensic Detection
- Physical Sciences › Computer Science › Artificial Intelligence › Adversarial Robustness in Machine Learning
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Generative Adversarial Networks and Image Synthesis
- Keywords
- adversarial attacks, statistical consistency, face forgery, distribution shift, image degradation, adversarial examples, white-box and black-box attacks
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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