Deep Neural Networks (DNN) are vulnerable to adversarial perturbations â small changes crafted deliberately on the input to mislead the model for wrong predictions. Adversarial attacks have disastrous consequences for deep learning empowered critical applications. Existing defense and detection techniques both require extensive knowledge of the model, testing inputs and even execution details. They are not viable for general deep learning implementations where the model internal is unknown, a common âblack-boxâ scenario for model users. Inspired by the fact that electromagnetic (EM) emanations of a model inference are dependent on both operations and data and may contain footprints of different input classes, we propose a framework, EMShepherd, to capture EM traces of model execution, perform processing on traces and exploit them for adversarial detection. Only benign samples and their EM traces are used to train the adversarial detector: a set of EM classifiers and class-specific unsupervised anomaly detectors. When the victim model system is under attack by an adversarial example, the model execution will be different from executions for the known classes, and the EM trace will be different. We demonstrate that our air-gapped EMShepherd can effectively detect different adversarial attacks on a commonly used FPGA deep learning accelerator for both Fashion MNIST and CIFAR-10 datasets. It achieves a detection rate on most types of adversarial samples, which is comparable to the state-of-the-art âwhite-boxâ software-based detectors.
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Evaluating Robustness of Sequence-based Deepfake Detector Models by Adversarial Perturbation
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 ð¿2-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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- Award ID(s):
- 2040209
- NSF-PAR ID:
- 10354760
- Date Published:
- Journal Name:
- Proceedings of the 1st Workshop on Security Implications of Deepfakes and Cheapfakes
- Page Range / eLocation ID:
- 13 to 18
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3494109.3527194
