Deepfake videos are getting better in quality and can be used for
dangerous disinformation campaigns. The pressing need to detect
these videos has motivated researchers to develop different types
of detection models. Among them, the models that utilize temporal information (i.e., sequence-based models) are more effective at
detection than the ones that only detect intra-frame discrepancies.
Recent work has shown that the latter detection models can be
fooled with adversarial examples, leveraging the rich literature on
crafting adversarial (still) images. It is less clear, however, how well
these attacks will work on sequence-based models that operate on
information taken over multiple frames. In this paper, we explore
the effectiveness of the Fast Gradient Sign Method (FGSM) and
the Carlini-Wagner 𝐿2-norm attack to fool sequence-based deepfake detector models in both the white-box and black-box settings.
The experimental results show that the attacks are effective with
a maximum success rate of 99.72% and 67.14% in the white-box
and black-box attack scenarios, respectively. This highlights the
importance of developing more robust sequence-based deepfake
detectors and opens up directions for future research.
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Explore the Transformation Space for Adversarial Images
Deep learning models are vulnerable to adversarial examples. Most of current adversarial attacks add pixel-wise perturbations restricted to some L^p-norm, and defense models are evaluated also on adversarial examples restricted inside L^p-norm balls. However, we wish to explore adversarial examples exist beyond L^p-norm balls and their implications for attacks and defenses. In this paper, we focus on adversarial images generated by transformations.
We start with color transformation and propose two gradient-based attacks. Since L^p-norm is inappropriate for measuring image quality in the transformation space, we use the similarity between transformations and the Structural Similarity Index. Next, we explore a larger transformation space consisting of combinations of color and affine transformations. We evaluate our transformation attacks on three data sets --- CIFAR10, SVHN, and ImageNet --- and their corresponding models. Finally, we perform retraining defenses to evaluate the strength of our attacks.
The results show that transformation attacks are powerful. They find high-quality adversarial images that have higher transferability and misclassification rates than C&W's L^p attacks, especially at high confidence levels. They are also significantly harder to defend against by retraining than C&W's L^p attacks. More importantly, exploring different attack spaces makes it more challenging to train a universally robust model.
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- Award ID(s):
- 1801751
- NSF-PAR ID:
- 10156933
- Date Published:
- Journal Name:
- ACM Conference on Data and Application Security and Privacy
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Accepted Manuscript1.0
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