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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2501.02147 |
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| _version_ | 1866929681508335616 |
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| author | Yadav, Umesh Niroula, Suman Gupta, Gaurav Kumar Yadav, Bicky |
| author_facet | Yadav, Umesh Niroula, Suman Gupta, Gaurav Kumar Yadav, Bicky |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_02147 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Exploring Secure Machine Learning Through Payload Injection and FGSM Attacks on ResNet-50 Yadav, Umesh Niroula, Suman Gupta, Gaurav Kumar Yadav, Bicky Cryptography and Security Machine Learning 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. |
| title | Exploring Secure Machine Learning Through Payload Injection and FGSM Attacks on ResNet-50 |
| topic | Cryptography and Security Machine Learning |
| url | https://arxiv.org/abs/2501.02147 |