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arXiv · 2501.02147

Exploring Secure Machine Learning Through Payload Injection and FGSM Attacks on ResNet-50

Umesh Yadav·Suman Niroula·Gaurav Kumar Gupta·Bicky Yadav
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Abstract

This paper investigates the resilience of a ResNet-50 image classification model under two prominent security threats: Fast Gradient Sign Method (FGSM) adversarial attacks and malicious payload injection. Initially, the model attains a 53.33% accuracy on clean images. When subjected to FGSM perturbations, its overall accuracy remains unchanged; however, the model's confidence in incorrect predictions notably increases. Concurrently, a payload injection scheme is successfully executed in 93.33% of the tested samples, revealing how stealthy attacks can manipulate model predictions without degrading visual quality. These findings underscore the vulnerability of even high-performing neural networks and highlight the urgency of developing more robust defense mechanisms for security-critical applications.

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Umesh Yadav, Suman Niroula, Gaurav Kumar Gupta, Bicky Yadav. 2025-01-04. Exploring Secure Machine Learning Through Payload Injection and FGSM Attacks on ResNet-50. https://arxiv.org/abs/2501.02147

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