Adversarial examples are intentionally crafted data with the purpose of deceiving neural networks into misclassification. When we talk about strategies to create such examples, we usually refer to perturbation-based methods that fabricate adversarial examples by applying invisible perturbations onto normal data. The resulting data reserve their visual appearance to human observers, yet can be totally unrecognizable to DNN models, which in turn leads to completely misleading predictions. In this paper, however, we consider crafting adversarial examples from existing data as a limitation to example diversity. We propose a non-perturbation-based framework that generates native adversarial examples from class-conditional generative adversarial networks.As such, the generated data will not resemble any existing data and thus expand example diversity, raising the difficulty in adversarial defense. We then extend this framework to pre-trained conditional GANs, in which we turn an existing generator into an "adversarial-example generator". We conduct experiments on our approach for MNIST and CIFAR10 datasets and have satisfactory results, showing that this approach can be a potential alternative to previous attack strategies.
Moreover, to gain control over the visual class of our generated adversarial examples , we used GANs in a conditioning setting @cite_0 @cite_28 . Conditional GAN (cGAN) @cite_28 , as the first proposed approach of conditioning GANs, takes an extra label vector @math as input in addition to the original random input @math . The class label input controls the visual class of image, whereas z provides image variability. The discriminator learns to tell the fake images @math from real images @math , while the generator learns to deceive the discriminator. Auxiliary Classifier GAN (ACGAN) @cite_0 , on the other hand, not only takes an extra input, but also gives an extra class probability output. More explicitly, the discriminator of ACGAN predicts both the image source (real or fake) and the image class, which functions as an auxiliary classifier. This modification helps stabilize training and improve image generation quality. We implemented both methods in this paper.
@cite_0 @cite_28
2950776302 2125389028
Synthesizing high resolution photorealistic images has been a long-standing challenge in machine learning. In this paper we introduce new methods for the improved training of generative adversarial networks (GANs) for image synthesis. We construct a variant of GANs employing label conditioning that results in 128x128 resolution image samples exhibiting global coherence. We expand on previous work for image quality assessment to provide two new analyses for assessing the discriminability and diversity of samples from class-conditional image synthesis models. These analyses demonstrate that high resolution samples provide class information not present in low resolution samples. Across 1000 ImageNet classes, 128x128 samples are more than twice as discriminable as artificially resized 32x32 samples. In addition, 84.7 of the classes have samples exhibiting diversity comparable to real ImageNet data. Generative Adversarial Nets [8] were recently introduced as a novel way to train generative models. In this work we introduce the conditional version of generative adversarial nets, which can be constructed by simply feeding the data, y, we wish to condition on to both the generator and discriminator. We show that this model can generate MNIST digits conditioned on class labels. We also illustrate how this model could be used to learn a multi-modal model, and provide preliminary examples of an application to image tagging in which we demonstrate how this approach can generate descriptive tags which are not part of training labels.