论文标题
通过更好的优化器来增强对神经网络的对抗性攻击
Boosting Adversarial Attacks on Neural Networks with Better Optimizer
论文作者
论文摘要
卷积神经网络在图像识别任务中表现优于人类，但它们仍然容易受到对抗例子的攻击。由于这些数据是通过在正常图像中添加不可察觉的噪声来制定的，因此它们的存在对深度学习系统构成了潜在的安全威胁。具有强大攻击性能的复杂对抗性示例也可以用作评估模型鲁棒性的工具。但是，在黑盒环境中，可以进一步提高对抗攻击的成功率。因此，这项研究将修改后的ADAM梯度下降算法与基于迭代梯度的攻击方法相结合。然后，使用拟议的ADAM迭代快速梯度方法来提高对抗性示例的可转移性。对成像网的广泛实验表明，所提出的方法比现有迭代方法提供了更高的攻击成功率。通过扩展我们的方法，我们在国防模型上实现了95.0％的最新攻击成功率。
Convolutional neural networks have outperformed humans in image recognition tasks, but they remain vulnerable to attacks from adversarial examples. Since these data are crafted by adding imperceptible noise to normal images, their existence poses potential security threats to deep learning systems. Sophisticated adversarial examples with strong attack performance can also be used as a tool to evaluate the robustness of a model. However, the success rate of adversarial attacks can be further improved in black-box environments. Therefore, this study combines a modified Adam gradient descent algorithm with the iterative gradient-based attack method. The proposed Adam Iterative Fast Gradient Method is then used to improve the transferability of adversarial examples. Extensive experiments on ImageNet showed that the proposed method offers a higher attack success rate than existing iterative methods. By extending our method, we achieved a state-of-the-art attack success rate of 95.0% on defense models.
