Abstract
At present, deep learning and convolutional neural networks are currently two of the fastest rising trends as the tool to perform a multitude of tasks such as image classification and computer vision. However, vulnerabilities in such networks can be exploited through input modification, leading to negative consequences to its users. This research aims to demonstrate an adversarial attack method that can hide its attack from human intuition in the form of a QR code, an entity that is most likely to conceal the attack from human acknowledgment due to its widespread use at the current time. A methodology was developed to demonstrate the QR-embedded adversarial patch creation process and attack existing CNN image classification models. Experiments were also performed to investigate trade-offs in different patch shapes and find the patch’s optimal color adjustment to improve scannability while retaining acceptable adversarial efficacy.
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Chindaudom, A., Sukasem, P., Benjasirimonkol, P., Sumonkayothin, K., Siritanawan, P., Kotani, K. (2020). AdversarialQR Revisited: Improving the Adversarial Efficacy. In: Yang, H., Pasupa, K., Leung, A.CS., Kwok, J.T., Chan, J.H., King, I. (eds) Neural Information Processing. ICONIP 2020. Communications in Computer and Information Science, vol 1332. Springer, Cham. https://doi.org/10.1007/978-3-030-63820-7_91
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DOI: https://doi.org/10.1007/978-3-030-63820-7_91
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