Sonic: Fast and Transferable Data Poisoning on Clustering Algorithms
<|reference_start|>Sonic: Fast and Transferable Data Poisoning on Clustering Algorithms: Data poisoning attacks on clustering algorithms have received limited attention, with existing methods struggling to scale efficiently as dataset sizes and feature counts increase. These attacks typically require re-clustering the entire dataset multiple times to generate predictions and assess the attacker's objectives, significantly hindering their scalability. This paper addresses these limitations by proposing Sonic, a novel genetic data poisoning attack that leverages incremental and scalable clustering algorithms, e.g., FISHDBC, as surrogates to accelerate poisoning attacks against graph-based and density-based clustering methods, such as HDBSCAN. We empirically demonstrate the effectiveness and efficiency of Sonic in poisoning the target clustering algorithms. We then conduct a comprehensive analysis of the factors affecting the scalability and transferability of poisoning attacks against clustering algorithms, and we conclude by examining the robustness of hyperparameters in our attack strategy Sonic.<|reference_end|>
@article{villani2024sonic:,
    title={Sonic: Fast and Transferable Data Poisoning on Clustering Algorithms},
    author={Francesco Villani, Dario Lazzaro, Antonio Emanuele Cin`a, Matteo
  Dell'Amico, Battista Biggio, Fabio Roli},
    journal={arXiv preprint arXiv:2408.07558},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.07558},
    primaryClass={cs.CR cs.CV cs.LG}
}