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Found 2 papers, 2 papers with code
advPattern: Physical-World Attacks on Deep Person Re-Identification via Adversarially Transformable Patterns
The results demonstrate that deep re-ID systems are vulnerable to our physical attacks.
Beyond Inferring Class Representatives: User-Level Privacy Leakage From Federated Learning
Although the state-of-the-art attacking techniques that incorporated the advance of Generative adversarial networks (GANs) could construct class representatives of the global data distribution among all clients, it is still challenging to distinguishably attack a specific client (i. e., user-level privacy leakage), which is a stronger privacy threat to precisely recover the private data from a specific client.
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