Generating Invariance-Based Adversarial Examples: Bringing Humans Back into the Loop

Author:

Merkle FlorianORCID,Sirbu Mihaela Roxana,Nocker MartinORCID,Schöttle PascalORCID

Publisher

Springer Nature Switzerland

Reference26 articles.

1. Athalye, A., Carlini, N., Wagner, D.: Obfuscated gradients give a false sense of security: circumventing defenses to adversarial examples. In: International Conference on Machine Learning, pp. 274–283. PMLR (2018)

2. Biggio, B., Roli, F.: Wild patterns: ten years after the rise of adversarial machine learning. Pattern Recogn. 84, 317–331 (2018)

3. Cadieu, C., et al.: Deep neural networks rival the representation of primate it cortex for core visual object recognition. PLoS Computa. Biol. 10, e1003963 (2014)

4. Carrasco, M.: Visual attention: the past 25 years. Vision. Res. 51, 1484–1525 (2011)

5. Carter, B., Jain, S., Mueller, J.W., Gifford, D.: Overinterpretation reveals image classification model pathologies. In: Advances in Neural Information Processing Systems, vol. 34 (2021)

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