  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.
