论文标题
MFI-PSO：深层神经网络的对抗图像生成的灵活有效方法
mFI-PSO: A Flexible and Effective Method in Adversarial Image Generation for Deep Neural Networks
论文作者
论文摘要
深度神经网络（DNNS）在图像分类方面取得了巨大的成功，但可能非常容易受到对抗性攻击的影响，并且对图像的扰动很小。为了改善DNN的对抗性图像的产生，我们开发了一种名为MFI-PSO的新颖方法，该方法利用了基于多种基于歧管的一阶影响度量，用于脆弱的图像和像素选择以及各种目标函数的粒子群优化。因此，我们的MFI-PSO可以在扰动像素的数量上有效地设计具有灵活的，自定义的选项，错误分类概率和目标不正确类的对抗图像。实验证明了我们的MFI-PSO在对抗攻击中的灵活性和有效性及其在某些流行方法上的吸引力。
Deep neural networks (DNNs) have achieved great success in image classification, but can be very vulnerable to adversarial attacks with small perturbations to images. To improve adversarial image generation for DNNs, we develop a novel method, called mFI-PSO, which utilizes a Manifold-based First-order Influence measure for vulnerable image and pixel selection and the Particle Swarm Optimization for various objective functions. Our mFI-PSO can thus effectively design adversarial images with flexible, customized options on the number of perturbed pixels, the misclassification probability, and the targeted incorrect class. Experiments demonstrate the flexibility and effectiveness of our mFI-PSO in adversarial attacks and its appealing advantages over some popular methods.
