AI safety robustness adversarial attacks defense

Implement AI safety measures including robustness testing, adversarial attack detection, and defense mechanisms for secure AI systems. Adversarial attacks: 1. FGSM (Fast Gradient Sign Method): single-step attack, epsilon perturbation, white-box scenario. 2. PGD (Projected Gradient Descent): iterative attack, stronger than FGSM, constrained optimization. 3. C&W attack: optimization-based, minimal distortion, confidence-based objective function. Defense mechanisms: 1. Adversarial training: include adversarial examples in training, robustness improvement, min-max optimization. 2. Defensive distillation: temperature scaling, smooth gradients, gradient masking prevention. 3. Input preprocessing: denoising, compression, randomized smoothing, transformation-based defenses. Robustness evaluation: 1. Certified defenses: mathematical guarantees, interval bound propagation, certified accuracy. 2. Empirical robustness: attack success rate, perturbation budget analysis, multiple attack types. 3. Natural robustness: corruption robustness, out-of-distribution generalization, real-world noise. Detection methods: 1. Statistical tests: input distribution analysis, feature statistics, anomaly detection. 2. Uncertainty quantification: prediction confidence, ensemble disagreement, Bayesian approaches. 3. Intrinsic dimensionality: manifold learning, adversarial subspace detection. Safety frameworks: 1. Alignment research: reward modeling, human feedback, value alignment, goal specification. 2. Interpretability: decision transparency, explanation generation, bias detection. 3. Monitoring systems: drift detection, performance degradation, safety constraints. Red teaming: systematic testing, failure mode discovery, stress testing, security assessment protocols, continuous monitoring for emerging threats and vulnerabilities.