Abstract
The susceptibility of current Deep Neural Networks (DNNs) to adversarial examples has been a significant concern in deep learning methods. In particular, sparse adversarial examples represent a specific category of adversarial examples that can deceive the target model by perturbing only a few pixels in images. While existing sparse adversarial attack methods have shown achievements, the current results of sparsity and efficiency are inadequate and require significant improvements. This paper introduces an adv-triplet loss and proposes a search attack method to attack the Face Expression Recognition (FER) system with minimal pixel perturbations. Specifically, we propose an adv-triplet loss function and utilize its gradient information to generate pixels for adversarial examples. Extensive experiments conducted on the CK+ and Oulu-CASIA datasets demonstrate the superiority of our proposed method over several state-of-the-art sparse attack methods.
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This work is supported by the National Natural Science Foundation of China (No.51975294).
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Li, W., Li, S., Shang, L. (2024). Adv-Triplet Loss for Sparse Attack on Facial Expression Recognition. In: Liu, F., Sadanandan, A.A., Pham, D.N., Mursanto, P., Lukose, D. (eds) PRICAI 2023: Trends in Artificial Intelligence. PRICAI 2023. Lecture Notes in Computer Science(), vol 14327. Springer, Singapore. https://doi.org/10.1007/978-981-99-7025-4_20
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DOI: https://doi.org/10.1007/978-981-99-7025-4_20
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