Battista Biggio

10th International Workshop on Artificial Intelligence and Security (AISec 2017)

2017

Artificial Intelligence (AI) and Machine Learning (ML) provide a set of useful analytic and decision-making techniques that are being leveraged by an ever-growing community of practitioners, including many whose applications have security-sensitive elements. However, while …

Machine Learning Security in Industry: A Quantitative Survey

2022 · arXiv (Cornell University)

Despite the large body of academic work on machine learning security, little is known about the occurrence of attacks on machine learning systems in the wild. In this paper, we report on a quantitative study …

Samples on Thin Ice: Re-Evaluating Adversarial Pruning of Neural Networks

2023

Neural network pruning has shown to be an effective technique for reducing the network size, trading desirable properties like generalization and robustness to adversarial attacks for higher sparsity. Recent work has claimed that adversarial pruning …

Silent Until Sparse: Backdoor Attacks on Semi-Structured Sparsity

2025 · arXiv (Cornell University)

Semi-structured (2:4) sparsity is a widely adopted pruning method in modern hardware and software ecosystems (e.g., NVIDIA Sparse Tensor Cores and PyTorch), achieving up to 2X faster inference and reduced memory footprint with negligible accuracy …