Discerning the Chaos: Detecting Adversarial Perturbations while Disentangling Intentional from Unintentional Noises
<|reference_start|>Discerning the Chaos: Detecting Adversarial Perturbations while Disentangling Intentional from Unintentional Noises: Deep learning models, such as those used for face recognition and attribute prediction, are susceptible to manipulations like adversarial noise and unintentional noise, including Gaussian and impulse noise. This paper introduces CIAI, a Class-Independent Adversarial Intent detection network built on a modified vision transformer with detection layers. CIAI employs a novel loss function that combines Maximum Mean Discrepancy and Center Loss to detect both intentional (adversarial attacks) and unintentional noise, regardless of the image class. It is trained in a multi-step fashion. We also introduce the aspect of intent during detection that can act as an added layer of security. We further showcase the performance of our proposed detector on CelebA, CelebA-HQ, LFW, AgeDB, and CIFAR-10 datasets. Our detector is able to detect both intentional (like FGSM, PGD, and DeepFool) and unintentional (like Gaussian and Salt & Pepper noises) perturbations.<|reference_end|>
@article{jain2024discerning,
    title={Discerning the Chaos: Detecting Adversarial Perturbations while
  Disentangling Intentional from Unintentional Noises},
    author={Anubhooti Jain, Susim Roy, Kwanit Gupta, Mayank Vatsa, and Richa Singh},
    journal={arXiv preprint arXiv:2409.19619},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.19619},
    primaryClass={cs.CV cs.AI}
}