Generation of Adversarial Mechanisms in Deep Neural Networks

Abstract

Deep learning is a subspace of intelligence system learning that experienced prominent results in almost all the application domains. However, Deep Neural Network found to be susceptible to perturbed inputs such that the model generates output other than the expected one. By including insignificant perturbation to the input effectuate computer vision models to make an erroneous prediction. Though, it is still a dilemma whether humans are prone to comparable errors. In this paper, we focus on this issue by leveraging the latest practices that help to generate adversarial examples in computer vision applications by considering diverse identified parameters, unidentified parameters, and architectures. The analysis of the distinct techniques has been done by considering different common parameters. Adversarial examples are easily transferable while designing computer vision applications that control the condition of the classifications of labels. The finding highlights that some methods like Zoo and Deepfool achieved 100% success for the nontargeted attack but are application-specific.

Associative Discussion Among Generating Adversarial Samples Using Evolutionary Algorithm and Samples Generated Using GAN

“…Pavate et al [20] discussed the different adversarial generations using gradient-based methods and concluded that calculating the gradient is practically difficult. Evolutionary algorithms (EAs) have been used to generate hostile examples.…”

“…The previous work concentrates on generating adversarial samples using gradient optimization methods that need internal design aspects of the model, such as several parameters for training, training data, and neural network type [6] [19]. Several adversarial samples are created without understanding the model's essential details, like the internal organization of the model [9] [20]. Evolutionary algorithms work only on the probability of labels from the target model; no internal details are required.…”