AdvCLIP: Downstream-agnostic Adversarial Examples in Multimodal Contrastive Learning

Multimodal contrastive learning aims to train a general-purpose feature extractor, such as CLIP, on vast amounts of raw, unlabeled paired image-text data. This can greatly benefit various complex downstream tasks, including cross-modal image-text retrieval and image classification. Despite its promising prospect, the security issue of cross-modal pre-trained encoder has not been fully explored yet, especially when the pre-trained encoder is publicly available for commercial use. In this work, we propose AdvCLIP, the first attack framework for generating downstream-agnostic adversarial examples based on cross-modal pre-trained encoders. AdvCLIP aims to construct a universal adversarial patch for a set of natural images that can fool all the downstream tasks inheriting the victim cross-modal pre-trained encoder. To address the challenges of heterogeneity between different modalities and unknown downstream tasks, we first build a topological graph structure to capture the relevant positions between target samples and their neighbors. Then, we design a topology-deviation based generative adversarial network to generate a universal adversarial patch. By adding the patch to images, we minimize their embeddings similarity to different modality and perturb the sample distribution in the feature space, achieving unviersal non-targeted attacks. Our results demonstrate the excellent attack performance of AdvCLIP on two types of downstream tasks across eight datasets. We also tailor three popular defenses to mitigate AdvCLIP, highlighting the need for new defense mechanisms to defend cross-modal pre-trained encoders.Comment: This paper has been accepted by the ACM International Conference on Multimedia (ACM MM '23, October 29-November 3, 2023, Ottawa, ON, Canada

Denial-of-Service or Fine-Grained Control: Towards Flexible Model Poisoning Attacks on Federated Learning

Federated learning (FL) is vulnerable to poisoning attacks, where adversaries corrupt the global aggregation results and cause denial-of-service (DoS). Unlike recent model poisoning attacks that optimize the amplitude of malicious perturbations along certain prescribed directions to cause DoS, we propose a Flexible Model Poisoning Attack (FMPA) that can achieve versatile attack goals. We consider a practical threat scenario where no extra knowledge about the FL system (e.g., aggregation rules or updates on benign devices) is available to adversaries. FMPA exploits the global historical information to construct an estimator that predicts the next round of the global model as a benign reference. It then fine-tunes the reference model to obtain the desired poisoned model with low accuracy and small perturbations. Besides the goal of causing DoS, FMPA can be naturally extended to launch a fine-grained controllable attack, making it possible to precisely reduce the global accuracy. Armed with precise control, malicious FL service providers can gain advantages over their competitors without getting noticed, hence opening a new attack surface in FL other than DoS. Even for the purpose of DoS, experiments show that FMPA significantly decreases the global accuracy, outperforming six state-of-the-art attacks.Comment: This paper has been accepted by the 32st International Joint Conference on Artificial Intelligence (IJCAI-23, Main Track

Throughput maximization for irregular reconfigurable intelligent surface assisted NOMA systems

Abstract Reconfigurable intelligent surface (RIS) is an emerging technology to improve the spectral efficiency of wireless communication systems. However, the high complexity of beam design and the non-negligible overhead associated with RIS limit the number of elements that can be deployed in practice. In this paper, we investigate the downlink communications of irregularly deployed intelligent reflecting surfaces that assist non-orthogonal multiple access (NOMA) systems. To address this challenge, we propose a novel four-step resource allocation algorithm. Specifically, we first obtain a sub-optimal solution for the sparse deployment of RIS elements using a Simulated Annealing Algorithm. We then solve the power allocation problem by employing an integer optimization algorithm that continuously iterates the immobile point. To simplify and optimize the reflection coefficient matrix, we propose a construction inequality algorithm. Finally, we optimize the channel assignment using a genetic algorithm. The simulation results demonstrate that the proposed irregular RIS-assisted NOMA system outperforms the traditional RIS-assisted orthogonal multiple access system, with a maximum throughput increase of approximately 30%