Adversarial Robustness Deep Learning is the study of neural network vulnerability to small input perturbations (adversarial examples) and development of robust models resistant to attacks — critical for deployment in adversarial settings. Adversarial robustness remains open challenge. Adversarial Examples small perturbations to input (imperceptible to humans) cause misclassification. Images: pixel-level noise. Text: character/word-level changes. Audio: imperceptible frequency shifts. Discovered by Szegedy et al. 2013. FGSM (Fast Gradient Sign Method) simple attack: perturb in direction of gradient toward wrong class. One-step attack, fast, often effective. Iterative Attacks IFGSM (Iterative FGSM): apply FGSM multiple steps, stronger attack. PGD (Projected Gradient Descent): optimal attack under L-infinity constraint. C&W Attack Carlini-Wagner: formulate adversarial example as optimization problem. Very effective, computationally expensive. Black-Box Attacks without model access. Transferability: adversarial examples for one model often fool others. Use substitute model. Query-based attacks estimate gradients via queries. Adversarial Training train on adversarial examples. Include adversarial perturbations in training data. Defense reduces accuracy-robustness tradeoff. Certified Defenses mathematically prove robustness bounds. Randomized smoothing: smoothed classifier certifiably robust. Verification methods (abstract interpretation, SAT solvers) prove no adversarial examples exist in region. Robustness Metrics L2 perturbation (Euclidean), L-infinity (max deviation), L0 (sparsity). Different norms have different attack strategies. TRADES (Trade-offs between Accuracy and Robustness) balance accuracy on clean data with adversarial robustness. Robust models sacrifice some clean accuracy. Evaluation Methodology properly evaluating robustness difficult. Adaptive attacks account for defense—sometimes 'defense' circumventable. Red teaming: adversary knows defense. Backdoor Attacks poisoning training data: specific patterns trigger misclassification. Defense: outlier detection, fine-tuning on clean data. Trojan Attacks similar to backdoor. Neural network Trojans activate under specific input pattern. Transferability adversarial examples transfer across models, architectures, datasets. Implies commonality in adversarial space. Interpretability and Adversarial Examples adversarial examples exploit model's feature representations. Saliency maps highlight features used. Robustness and Interpretability Link more interpretable models might be more robust? Unclear relationship. Geometry of Adversarial Space adversarial examples lie near decision boundary. Robust models have larger margins. Defense Mechanisms many proposed: defensive distillation, neural network purification, ensemble methods. Most have been broken. Perturbation Budgets maximum allowed perturbation epsilon. Smaller epsilon easier to defend (larger epsilon harder). Poisoning vs. Evasion poisoning: attack during training, evasion: attack at test time. Certified Perturbation Bounds formal bounds: if model is ε-robust, adversarial perturbation magnitude guaranteed bounded. Applications and Deployment autonomous vehicles (adversarial stop sign), biometric systems (spoofing), medical imaging (misdiagnosis). Current State perfect robustness infeasible. Practical deployment uses modest robustness with detection. Robustness Benchmarks RobustBench: standardized robustness evaluation, model comparison. Open Questions fundamental limits of robustness: is accuracy-robustness tradeoff inherent? Adversarial robustness remains active research** with significant practical implications.