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Found 4 papers, 0 papers with code
Boosting the Transferability of Adversarial Examples via Local Mixup and Adaptive Step Size
Adversarial examples are one critical security threat to various visual applications, where injected human-imperceptible perturbations can confuse the output. Generating transferable adversarial examples in the black-box setting is crucial but challenging in practice.
Making Recommender Systems Forget: Learning and Unlearning for Erasable Recommendation
The basic idea of most recommender systems is collaborative filtering, but existing MU methods ignore the collaborative information across users and items.
Similarity-based Label Inference Attack against Training and Inference of Split Learning
We mathematically analyze the potential label leakages and propose the cosine and Euclidean similarity measurements for gradients and smashed data, respectively.
Energon: Towards Efficient Acceleration of Transformers Using Dynamic Sparse Attention
To enable such an algorithm with lower latency and better energy efficiency, we also propose an Energon co-processor architecture.
