1 research outputs found
Generating Adversarial Examples With Conditional Generative Adversarial Net
Recently, deep neural networks have significant progress and successful
application in various fields, but they are found vulnerable to attack
instances, e.g., adversarial examples. State-of-art attack methods can generate
attack images by adding small perturbation to the source image. These attack
images can fool the classifier but have little impact to human. Therefore, such
attack instances are difficult to generate by searching the feature space. How
to design an effective and robust generating method has become a spotlight.
Inspired by adversarial examples, we propose two novel generative models to
produce adaptive attack instances directly, in which conditional generative
adversarial network is adopted and distinctive strategy is designed for
training. Compared with the common method, such as Fast Gradient Sign Method,
our models can reduce the generating cost and improve robustness and has about
one fifth running time for producing attack instance.Comment: 6 page