Generative Adversarial Examples

24 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Certified Robustness to Adversarial Examples with Differential Privacy (2018, neutral): “Recently, non-norm attacks have been formalized [51].”
Excessive Invariance Causes Adversarial Vulnerability (2018, background): “…recent work argued that the perturbation-based approach is unrealistically restrictive and called for the need of generalizing the concept of adversarial examples to the unrestricted case, including any input crafted to be misinterpreted by the learned model (Song et al., 2018; Brown et al., 2018).”
On Evaluating Adversarial Robustness (2019, background): “However, at this time the authors need to completely 5See for example the work of Sharma & Chen (2017); Song et al. (2018) who explicitly step outside of the threat model of the original defenses to evaluate their robustness. switch their frame of mind and try as hard as possible to show their…”
Disentangling Adversarial Robustness and Generalization (2018, background): “Recently, generative models have also been utilized to craft – or learn – more natural adversarial examples [88, 11, 107, 80].”
Advances in adversarial attacks and defenses in computer vision: A survey (2021, background): “Nevertheless, there is also evidence of on-manifold adversarial examples [43], [314], [315].”