On Fooling Facial Recognition Systems using Adversarial Patches

Parmar, Rushirajsinh; Kuribayashi, Minoru; Takiwaki, Hiroto; Raval, Mehul S.

doi:10.1109/ijcnn55064.2022.9892071

Cited by 4 publications

(1 citation statement)

References 10 publications

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“…However, current adversarial attacks in face recognition are specifically designed for an individual target identity, requiring the regeneration of perturbations for every application. For instance, in generating a tiny patch on the face [11], the adversarial samples can mislead the face recognition network. Additionally, there are adversarial samples of facial makeup generated using generative adversarial networks (GANs) [12], which exploit facial features to generate adversarial samples with specific makeup to deceive the target model.…”

Section: Introductionmentioning

confidence: 99%

KRT-FUAP: Key Regions Tuned via Flow Field for Facial Universal Adversarial Perturbation

Jin,

Liu,

Sun

et al. 2024

Applied Sciences

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It has been established that convolutional neural networks are susceptible to elaborate tiny universal adversarial perturbations (UAPs) in natural image classification tasks. However, UAP attacks against face recognition systems have not been fully explored. This paper proposes a spatial perturbation method that generates UAPs with local stealthiness by learning variable flow field to fine-tune facial key regions (KRT-FUAP). We ensure that the generated adversarial perturbations are positioned within reasonable regions of the face by designing a mask specifically tailored to facial key regions. In addition, we pay special attention to improving the effectiveness of the attack while maintaining the stealthiness of the perturbation and achieve the dual optimization of aggressiveness and stealthiness by accurately controlling the balance between adversarial loss and stealthiness loss. Experiments conducted on the frameworks of IResNet50 and MobileFaceNet demonstrate that our proposed method achieves an attack performance comparable to existing natural image universal attack methods, but with significantly improved stealthiness.

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Section: Introductionmentioning

confidence: 99%

KRT-FUAP: Key Regions Tuned via Flow Field for Facial Universal Adversarial Perturbation

Jin,

Liu,

Sun

et al. 2024

Applied Sciences

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Privacy Protection Against Automated Tracking System Using Adversarial Patch

Takiwaki

Kuribayashi

Funabiki

et al. 2022

2022 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC)

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A Momentum-Based Local Face Adversarial Example Generation Algorithm

et al. 2022

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Small perturbations can make deep models fail. Since deep models are widely used in face recognition systems (FRS) such as surveillance and access control, adversarial examples may introduce more subtle threats to face recognition systems. In this paper, we propose a practical white-box adversarial attack method. The method can automatically form a local area with key semantics on the face. The shape of the local area generated by the algorithm varies according to the environment and light of the character. Since these regions contain major facial features, we generated patch-like adversarial examples based on this region, which can effectively deceive FRS. The algorithm introduced the momentum parameter to stabilize the optimization directions. We accelerated the generation process by increasing the learning rate in segments. Compared with the traditional adversarial algorithm, our algorithms are very inconspicuous, which is very suitable for application in real scenes. The attack was verified on the CASIA WebFace and LFW datasets which were also proved to have good transferability.

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On Fooling Facial Recognition Systems using Adversarial Patches

Cited by 4 publications

References 10 publications

KRT-FUAP: Key Regions Tuned via Flow Field for Facial Universal Adversarial Perturbation

KRT-FUAP: Key Regions Tuned via Flow Field for Facial Universal Adversarial Perturbation

Privacy Protection Against Automated Tracking System Using Adversarial Patch

A Momentum-Based Local Face Adversarial Example Generation Algorithm

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