Adversarial Attacks

Unexpected Weaknesses in Neural Network Visual Classification Algorithms

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Detecting Adversarial Samples Using Influence Functions and Nearest Neighbors

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Detecting Adversarial Samples Using Influence Functions and Nearest Neighbors

Reference Cohen G, Sapiro G, Giryes R. Detecting adversarial samples using influence functions and nearest neighbors[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2020: 14453-14462. Abstract Deep neural networks are notorious for being vulnerable to adversarial attacks, which involve adding small perturbations to input images to mislead their predictions. Therefore, detecting adversarial … Read more