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(∇xJ(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
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