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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63 citations
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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54 citations
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 background in this paper
...Deep learning models have achieved state-of-the-art performance in various computer vision related tasks such as object detection and face recognition [18, 24]....
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"SmartBox: Benchmarking Adversarial ..." refers background or methods in this paper
...Adversarial Training: In adversarial training [33], a new model is trained using the original dataset and adversarial examples with their correct labels....
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...[33] Trains a new model on original and adversarial training images....
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"SmartBox: Benchmarking Adversarial ..." refers background or methods in this paper
...FGSM [15]: It computes the gradient of the loss function of the model concerning the image vector to get the direction of pixel change....
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...[15] Computes gradient of the loss function w....
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...While whitebox attacks such as ElasticNet (EAD) [6], DeepFool [28], L2 [5], Fast Gradient Sign Method (FGSM) [15], Projective Gradient Descent (PGD) [26], and MI-FGSM [10] have complete access and information about the trained network, blackbox attacks such as one pixel attack [32] and universal perturbations [27] have no information about the trained Deep Neural Network (DNN)....
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...While whitebox attacks such as ElasticNet (EAD) [6], DeepFool [28], L2 [5], Fast Gradient Sign Method (FGSM) [15], Projective Gradient Descent (PGD) [26], and MI-FGSM [10] have complete access and information about the trained network, blackbox attacks such as one pixel attack [32] and universal perturbations [27]...
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...FGSM perturbations can be computed by minimizing either the L1, L2 or L∞ norm....
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6,528 citations