论文标题
通过海绵中毒发作的能量延迟攻击
Energy-Latency Attacks via Sponge Poisoning
论文作者
论文摘要
海绵示例是优化的测试时间输入，以增加部署在硬件加速器上的深网的能耗和预测潜伏期。通过增加分类过程中激活的神经元的分数，这些攻击减少了网络激活模式中的稀疏性，从而恶化了硬件加速器的性能。在这项工作中，我们提出了一种新颖的训练时间攻击，称为海绵中毒，该攻击旨在使神经网络在任何测试输入中的能量消耗和预测潜伏期恶化，而不会影响分类的准确性。为了进行这次攻击，我们假设攻击者只能控制训练期间的一些模型更新 - 例如，当模型培训将模型培训外包给不受信任的第三方或通过联合学习分发时。我们在图像分类任务上进行的广泛实验表明，海绵中毒是有效的，并且对其进行修复的微调中毒模型为大多数用户造成了巨大的成本，这强调了解决海绵中毒仍然是一个空旷的问题。
Sponge examples are test-time inputs optimized to increase energy consumption and prediction latency of deep networks deployed on hardware accelerators. By increasing the fraction of neurons activated during classification, these attacks reduce sparsity in network activation patterns, worsening the performance of hardware accelerators. In this work, we present a novel training-time attack, named sponge poisoning, which aims to worsen energy consumption and prediction latency of neural networks on any test input without affecting classification accuracy. To stage this attack, we assume that the attacker can control only a few model updates during training -- a likely scenario, e.g., when model training is outsourced to an untrusted third party or distributed via federated learning. Our extensive experiments on image classification tasks show that sponge poisoning is effective, and that fine-tuning poisoned models to repair them poses prohibitive costs for most users, highlighting that tackling sponge poisoning remains an open issue.
