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Found 5 papers, 2 papers with code
Defending Substitution-Based Profile Pollution Attacks on Sequential Recommenders
Additionally, we design an adversarial training method tailored for sequential recommender systems.
Efficient Localness Transformer for Smart Sensor-Based Energy Disaggregation
Modern smart sensor-based energy management systems leverage non-intrusive load monitoring (NILM) to predict and optimize appliance load distribution in real-time.
Black-Box Attacks on Sequential Recommenders via Data-Free Model Extraction
Under this setting, we propose an API-based model extraction method via limited-budget synthetic data generation and knowledge distillation.
Certified Defense via Latent Space Randomized Smoothing with Orthogonal Encoders
Randomized Smoothing (RS), being one of few provable defenses, has been showing great effectiveness and scalability in terms of defending against $\ell_2$-norm adversarial perturbations.
Are Adversarial Examples Created Equal? A Learnable Weighted Minimax Risk for Robustness under Non-uniform Attacks
Adversarial Training is proved to be an efficient method to defend against adversarial examples, being one of the few defenses that withstand strong attacks.
