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Found 4 papers, 3 papers with code
DRSM: De-Randomized Smoothing on Malware Classifier Providing Certified Robustness
After showing how DRSM is theoretically robust against attacks with contiguous adversarial bytes, we verify its performance and certified robustness experimentally, where we observe only marginal accuracy drops as the cost of robustness.
On the Effectiveness of Regularization Against Membership Inference Attacks
Finally, we quantify the opportunity of future MIAs to compromise privacy by designing a white-box `distance-to-confident' (DtC) metric, based on adversarial sample crafting.
Shallow-Deep Networks: Understanding and Mitigating Network Overthinking
Overthinking is computationally wasteful, and it can also be destructive when, by the final layer, a correct prediction changes into a misclassification.
Poison Frogs! Targeted Clean-Label Poisoning Attacks on Neural Networks
The proposed attacks use "clean-labels"; they don't require the attacker to have any control over the labeling of training data.
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