Abstract
Deep neural networks (DNN’s) have achieved state-of-the-art (SOTA) for many image-related tasks like classification, object detection, etc. The authors have shown that DNN’s can be easily fooled by adversarial examples that can be generated by adding small distortions or perturbations to the images. Several attack methods to add these perturbations in images have been proposed like the Fast Gradient Sign Method (FGSM), Projected Gradient Descent (PGD), etc. to create distortions in images that cause the DNN’s to misclassify instances. It is essential to come up with strategies to identify such fake images. Kee** this in mind, we propose an end-to-end pipeline capable of detecting adversarially attacked images, i.e., images with perturbations. For a given image, we generate images with manipulation masks using Mantra-Net and pass them through a trained binary classifier that distinguishes between perturbed and original images. Experiments on the ImageNet Large Scale Visual Recognition Challenge (ILSVRC) 2012 dataset have shown that our pipeline achieves an accuracy of 0.986 on the task of detecting adversarially crafted images.
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Vohra, A., Reddy, N., Dhawale, K., Jain, P. (2023). Robust Pipeline for Detection of Adversarial Images. In: Sisodia, D.S., Garg, L., Pachori, R.B., Tanveer, M. (eds) Machine Intelligence Techniques for Data Analysis and Signal Processing. Lecture Notes in Electrical Engineering, vol 997. Springer, Singapore. https://doi.org/10.1007/978-981-99-0085-5_32
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DOI: https://doi.org/10.1007/978-981-99-0085-5_32
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