Adversarial Examples to Fool Iris Recognition Systems Adversarial examples have recently proven to be able to fool deep learning methods by adding carefully crafted small perturbation to the input space image. In this paper, we study the possibility of generating adversarial examples for codebased iris recognition systems. Since generating adversarial examples requires backpropagation of the adversarial loss, conventional filter bankbased iriscode generation frameworks cannot be employed in such a setup. Therefore, to compensate for this shortcoming, we propose to train a deep autoencoder surrogate network to mimic the conventional iris code generation procedure. This trained surrogate network is then deployed to generate the adversarial examples using the iterative gradient sign method algorithm 15. We consider nontargeted and targeted attacks through three attack scenarios. Considering these attacks, we study the possibility of fooling an iris recognition system in whitebox and blackbox frameworks.

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.