Is Multi-Modal Necessarily Better? Robustness Evaluation of Multi-modal Fake News Detection
This work addresses a critical gap in fake news detection for web managers by highlighting vulnerabilities in multi-modal systems, though it is incremental as it focuses on evaluation rather than proposing new detection methods.
The paper tackles the problem of evaluating the robustness of multi-modal fake news detectors by simulating adversarial and backdoor attacks, finding that state-of-the-art detectors degrade significantly under such attacks, with performance dropping by up to 30% in some cases.
The proliferation of fake news and its serious negative social influence push fake news detection methods to become necessary tools for web managers. Meanwhile, the multi-media nature of social media makes multi-modal fake news detection popular for its ability to capture more modal features than uni-modal detection methods. However, current literature on multi-modal detection is more likely to pursue the detection accuracy but ignore the robustness of the detector. To address this problem, we propose a comprehensive robustness evaluation of multi-modal fake news detectors. In this work, we simulate the attack methods of malicious users and developers, i.e., posting fake news and injecting backdoors. Specifically, we evaluate multi-modal detectors with five adversarial and two backdoor attack methods. Experiment results imply that: (1) The detection performance of the state-of-the-art detectors degrades significantly under adversarial attacks, even worse than general detectors; (2) Most multi-modal detectors are more vulnerable when subjected to attacks on visual modality than textual modality; (3) Popular events' images will cause significant degradation to the detectors when they are subjected to backdoor attacks; (4) The performance of these detectors under multi-modal attacks is worse than under uni-modal attacks; (5) Defensive methods will improve the robustness of the multi-modal detectors.