A LabelBased Approach for Automatic Identifying Adversarial Examples with Image Transformation Besides extraordinary results that deep neural networks are widely deployed in many fields such as computer vision, speech recognition, and natural language processing, in recent years deep neural networks have been recognized vulnerable to intentional modification of legitimate inputs called adversarial examples. These patterns are almost indistinguishable from AI models and human perception. Adversarial examplesu0027 concern is rising dramatically and is attracted by many researchu0027s apprehensiveness because of its tremendous impact. Unfortunately, until now there is none of the defenses has been shown to be very effective. In this paper, we introduce a new defense strategy against adversarial examples by using a labelbased endtoend system. Our proposed defense system can mostly distinguish adversarial samples and benign images without human intervention. We exploit the important role of spatial domain in adversarial attacks and proposing a stateoftheart method for detecting adversarial examples based on our observation. We evaluate our systemu0027s performance on a variety of standard benchmark datasets including MNIST and ImageNet. Our proposed method reached out detection rates in a range from 94.6% to 99.2% in many settings.

Generation of Low Distortion Adversarial Attacks via Convex Programming As deep neural networks (DNNs) achieve extraordinary performance in a wide range of tasks, testing their robustness under adversarial attacks becomes paramount. Adversarial attacks, also known as adversarial examples, are used to measure the robustness of DNNs and are generated by incorporating imperceptible perturbations into the input data with the intention of altering a DNNu0027s classification. In prior work in this area, most of the proposed optimization based methods employ gradient descent to find adversarial examples. In this paper, we present an innovative method which generates adversarial examples via convex programming. Our experiment results demonstrate that we can generate adversarial examples with lower distortion and higher transferability than the Cu0026W attack, which is the current stateoftheart adversarial attack method for DNNs. We achieve 100% attack success rate on both the original undefended models and the adversariallytrained models. Our distortions of the L_inf attack are respectively 31% and 18% lower than the Cu0026W attack for the best case and average case on the CIFAR10 data set.