The efficacy of availability poisoning, a method of poisoning data by
injecting imperceptible perturbations to prevent its use in model training, has
been a hot subject of investigation. Previous research suggested that it was
difficult to effectively counteract such poisoning attacks. However, the
introduction of various defense methods has challenged this notion. Due to the
rapid progress in this field, the performance of different novel methods cannot
be accurately validated due to variations in experimental setups. To further
evaluate the attack and defense capabilities of these poisoning methods, we
have developed a benchmark -- APBench for assessing the efficacy of adversarial
poisoning. APBench consists of 9 state-of-the-art availability poisoning
attacks, 8 defense algorithms, and 4 conventional data augmentation techniques.
We also have set up experiments with varying different poisoning ratios, and
evaluated the attacks on multiple datasets and their transferability across
model architectures. We further conducted a comprehensive evaluation of 2
additional attacks specifically targeting unsupervised models. Our results
reveal the glaring inadequacy of existing attacks in safeguarding individual
privacy. APBench is open source and available to the deep learning community:
this https URL

评估可用性中毒方法及其对隐私的保护能力，提出 APBench 用于评估对抗性中毒攻击的有效性