ML Security

• Backdoor Attack on Neural Networks Detection: What Works

  Backdoor detection splits into data, model and input checks. How spectral signatures, Neural Cleanse, STRIP and MNTD work, and where they fail.

• How Adversarial Examples Fool Image Classifiers

  Adversarial examples follow the model's own gradient: a 0.007 per-pixel nudge made GoogLeNet call a panda a gibbon at 99.3%. How and why it works.

• Preprocessing Defenses Fail on Depthwise-Separable Edge CNNs

  A new preprint: six preprocessing defenses recover a residual CNN but not depthwise-separable ones, inverting the signal squeeze detectors read.

• How FGSM Adversarial Attacks Work: Fast Gradient Sign Method

  FGSM adds one signed gradient step to every pixel to flip a classifier's prediction. The update rule, epsilon trade-offs, and why PGD replaced it.

• CLIP Adversarial Attacks: One Perturbation, Every Model

  CLIP is the shared vision encoder under most multimodal systems, which is why one universal perturbation now transfers across encoders, tasks and domains.

• Adversarial Attack Libraries: ART, Foolbox, torchattacks

  ART, Foolbox, torchattacks, CleverHans and TextAttack compared on scope, framework support, attack coverage, licence and repository maintenance.

• Data Poisoning Attack Detection Methods That Actually Work

  A practitioner's rundown of data poisoning attack detection methods: spectral signatures, activation clustering, and what OWASP and NIST recommend.

• Adversarial Training Best Practices That Survive Evaluation

  A practitioner's guide to adversarial training: pick a threat model, run PGD-AT correctly, catch robust overfitting, and evaluate with AutoAttack.

• Adversarial Examples vs. Data Poisoning

  Adversarial examples attack a deployed model at inference; data poisoning attacks the model before it is deployed. Different timing, different defenses.

• Adversarial Robustness in NLP: Why Text Attacks Differ

  Discrete input spaces, semantic constraints, and human-perceptibility rules change what counts as an adversarial example in text.

• Data Poisoning and Backdoor Attacks on Foundation Models

  Training data manipulation, backdoor triggers, and Trojan attacks against foundation models: what the threat model requires and which defenses hold up.

• Adversarial Transferability: How Black-Box Attacks Work

  Adversarial examples transfer across models with different architectures and training sets. Why that happens, and what it means for black-box defenses.

• Certified Robustness via Randomized Smoothing

  Randomized smoothing gives a provable robustness radius. What that certificate actually covers, where it breaks at realistic epsilon, and when to use it.

• Membership Inference Attacks on Production ML APIs

  Shokri et al.'s shadow-model attack is the canonical reference, but the gap between the paper's threat model and a real rate-limited API is wide.

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