Multimodal Adversarial Defense Trained on Features Extracted from Images and Brain Activity

Deep neural networks (DNNs) have achieved significant advancements in artificial intelligence (AI) but remain vulnerable to adversarial attacks. In contrast, the human cognitive system, integrating multimodal processing and brain activity, shows high robustness against such attacks. This paper intro...

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Bibliographic Details
Published inIEEE Global Conference on Consumer Electronics pp. 1183 - 1184
Main Authors Nakajima, Tasuku, Maeda, Keisuke, Togo, Ren, Ogawa, Takahiro, Haseyama, Miki
Format Conference Proceeding
LanguageEnglish
Published IEEE 29.10.2024
Subjects
Accuracy
Artificial neural networks
Brain
Data mining
Data models
Feature extraction
Functional magnetic resonance imaging
Image classification
Robustness
Visualization
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Summary:Deep neural networks (DNNs) have achieved significant advancements in artificial intelligence (AI) but remain vulnerable to adversarial attacks. In contrast, the human cognitive system, integrating multimodal processing and brain activity, shows high robustness against such attacks. This paper introduces a novel multimodal adversarial defense method that incorporates brain activity data to enhance the DNN robustness. We use functional magnetic resonance imaging (fMRI) data recorded while subjects view images and combine it with visual data to create a more robust classifier. Experimental results using the NSD dataset demonstrate that our method improves robustness against adversarial attacks compared to traditional methods. This research underscores the potential of leveraging brain activity data to develop more robust AI systems.
ISSN:2693-0854
DOI:10.1109/GCCE62371.2024.10760752