Adversarial Generative Nets: Neural Network Attacks on State-of-the-Art Face Recognition

By Mahmood Sharif, Sruti Bhagavatula, Lujo Bauer, Michael K. Reiter. 2018

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Citations in the corpus

Wasserstein Adversarial Examples via Projected Sinkhorn Iterations (2019, methodology): “Some examples include wearing adversarial 3D printed glasses to fool facial recognition (Sharif et al., 2017), the use of adversarial graffiti to attack traffic sign classification (Eykholt et al.”
Transfer of Adversarial Robustness Between Perturbation Types (2019, background): “Meanwhile, researchers continue to develop creative attacks that are difficult to even mathematically specify, such as fake eyeglasses, adversarial stickers, and 3D-printed objects (Sharif et al., 2018; Brown et al., 2017; Athalye et al., 2017).”
Deep Face Recognition: A Survey (2018, background): “Although face anti-spoofing has achieved successes in resisting print, replay, and 3D mask attacks, a new type of misclassification attack via physically realizable attack artifacts [140], [139] causes a rising interest, proving that FR systems are still not robust and are vulnerable to attacks.”
Black-box Adversarial Attacks with Limited Queries and Information (2018, background): “Several papers have investigated practical black-box attacks on real-world systems such as speech recognition systems (Carlini et al., 2016), malware detectors (Hu & Tan, 2017; Xu et al., 2016), and face recognition systems (Sharif et al., 2017).”
Enhancing Adversarial Example Transferability With an Intermediate Level Attack (2019, background): “Much of the literature has been dedicated to the development of new attacks that use different perceptibility metrics [2, 28, 26], security settings (black box/white box) [23, 1], as well as increasing efficiency [8].”
Efficient Decision-Based Black-Box Adversarial Attacks on Face Recognition (2019, background): “The adversarial eyeglasses can also be produced by generative networks [25].”