SmartBox: Benchmarking Adversarial Detection and Mitigation Algorithms for Face Recognition
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Cites methods from "SmartBox: Benchmarking Adversarial ..."
...Recently, Goel et al. (2018) have prepared the SmartBox toolbox containing several existing adversarial generation, detection, and mitigation algorithms....
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Cites background from "SmartBox: Benchmarking Adversarial ..."
...However, the noisy structure of the perturbation makes these attacks vulnerable against conventional defense methods such as quantizing [18], smoothing [6] or training on adversarial examples [30]....
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Cites background from "SmartBox: Benchmarking Adversarial ..."
...[10] have developed a toolbox containing various algorithm corresponds to adversarial generation, detection, and mitigation....
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53 citations
Cites background or methods from "SmartBox: Benchmarking Adversarial ..."
...Further, Goel et al. (2018) developed the first benchmark toolbox of algorithms for adversarial generation, detection, and mitigation for face recognition....
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...t the attacks performed using image-agnostic perturbations (i.e., one noise across multiple images) can be detected using a computationally efficient algorithm based on the data distribution. Further, Goel et al. (2018) developed the first benchmark toolbox of algorithms for adversarial generation, detection, and mitigation for face recognition. Recently, Goel et al. (2019) presented one of the best security mechanis...
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References
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"SmartBox: Benchmarking Adversarial ..." refers methods in this paper
...[17] uses Maximum Mean Discrepancy (MMD) test that statistically differentiates adversarial images from the original images....
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