Adversarial Attacks on Deep Neural Networks: Vulnerabilities and Mitigation Strategies

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Adversarial Attacks on Deep Neural Networks

Adversarial examples exploit the sensitivity of deep neural networks (DNNs) to imperceptible perturbations. The Fast Gradient Sign Method (FGSM) computes adversarial examples via 

 x′=x+ϵ⋅sign(∇x​J(θ,x,y))

 Key Uses:

Testing robustness of computer vision models in autonomous vehicles (e.g., attacking YOLO detectors) and malware classification systems.

 Hacks & Defences:

 Adversarial training augments the training dataset with perturbed samples generated by Projected Gradient Descent (PGD).

Use feature squeezing and randomization at inference time. Black-box attack hack: Train a substitute model to approximate the target’s decision boundary. Physical-world hack: Generate robust adversarial patches that survive real-world lighting variations.

 References:

Goodfellow, I.J., et al. (2014). Explaining and Harnessing Adversarial Examples. arXiv:1412.6572.

Madry, A., et al. (2018). Towards Deep Learning Models Resistant to Adversarial Attacks. ICLR