Adversarial Attacks on ML Models: Defense Strategies

Adversarial Machine Learning

Attackers can manipulate ML models through carefully crafted inputs, exploiting model vulnerabilities.

Attack Types

Evasion Attacks

Modify inputs to cause misclassification

Perturbation-based

Query-based

Poisoning Attacks

Corrupt training data

Backdoor insertion

Model manipulation

Model Extraction

Steal model functionality

Query-based extraction

API abuse

Inference Attacks

Membership inference

Model inversion

Attribute inference

Attack Techniques

Image Domain

FGSM (Fast Gradient Sign Method)

PGD (Projected Gradient Descent)

C&W Attack

Text Domain

Character manipulation

Word substitution

Sentence paraphrasing

Defense Strategies

Adversarial Training

Include adversarial examples

Robust optimization

Input Preprocessing

Detection mechanisms

Input transformation

Denoising

Model Architecture

Defensive distillation

Certified defenses

Ensemble methods

Detection

Statistical detection

Adversarial detectors

Input validation

Best Practices

Assume attacks will occur

Threat modeling

Layered defenses

Monitoring

Regular testing

Evaluation

Robust accuracy

Attack success rate

Defense overhead

Conclusion

Security must be a first-class consideration in ML system design.