12 accepted papers
Federated Learning (FL) enables privacy-preserving distributed training but remains vulnerable to backdoor attacks. Attackers can embed malicious trigger-label associations into the global model by participating in the aggregation process. Existing defense methods typically defend against backdoor a
Cross-domain recommendation (CDR) serves as a pivotal solution to data sparsity and cold-start problems by transferring knowledge across distinct domains. However, existing approaches predominately rely on Euclidean embedding spaces, which suffer from a fundamental geometry-distribution mismatch: re…
As large language models (LLMs) scale, their inference incurs substantial computational resources, exposing them to energy-latency attacks, where crafted prompts induce high energy and latency cost. Existing attack methods aim to prolong output by delaying the generation of termination symbols. Howe
Vision Language Models (VLMs) achieve strong performance across multi-modal tasks but remain vulnerable to universal adversarial perturbations (UAPs). Existing UAP methods mainly operate on the visual modality, overlooking structured textual semantics and cross-modal interactions, which limits their…
Data-free black-box attacks aim to attack a model without access to either the model parameters or training data. Existing methods use a generator to synthesize training samples and then train a substitute model to imitate the victim model. The adversarial examples (AEs) are finally generated using…
Cross-Domain Sequential Recommendation (CDSR) leverages user behaviors across multiple domains to mitigate data sparsity and cold-start challenges in Single-Domain Sequential Recommendation. Existing methods primarily rely on shared users (overlapping users) to learn transferable interest representa
Second-order optimization algorithms, such as the Newton method and the natural gradient descent (NGD) method exhibit excellent convergence properties for training deep neural networks, but the high computational cost limits its practical application. In this paper, we focus on the NGD method and pr…
Genomic selection (GS), as a critical crop breeding strategy, plays a key role in enhancing food production and addressing the global hunger crisis. The predominant approaches in GS currently revolve around employing statistical methods for prediction. However, statistical methods often come with tw…
Existing black-box model inversion attacks mainly focus on training and attacking surrogate models. However, due to the deployment process of face recognition models, training surrogate models becomes extremely difficult in practice. At the same time, query-based black-box inversion attacks still su…
Videos have a higher dimensionality compared with images, making adversarial video attacks more challenging. We propose a gradient-based method for self-adaptive white-box video keyframe selection and video adversarial example generation, taking advantage of that perturbations are transferable betwe…
Second-order optimization methods have the ability to accelerate convergence by modifying the gradient through the curvature matrix. There have been many attempts to use second-order optimization methods for training deep neural networks. In this work, inspired by diagonal approximations and factore…
It is well-known that second-order optimizer can accelerate the training of deep neural networks, however, the huge computation cost of second-order optimization makes it impractical to apply in real practice. In order to reduce the cost, many methods have been proposed to approximate a second-order…