Person:
Bhilare, Shruti

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Shruti Bhilare

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Faculty

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079-68261651

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Biometrics, Pattern Recognition, Image Processing

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Biography

Dr. Shruti Bhilare is an assistant professor in Dhirubhai Ambani Institute of Information and Communication Technology, Gandhinagar since July 2019. She received her Ph.D. degree in Computer Science and Engineering from Indian Institute of Technology Indore, India. Her research interests include pattern recognition and image processing with focus on biometric applications. She received her B.E. and M.E. (with specialization in Software Engineering) degrees in Computer Engineering from Institute of Engineering and Technology, Devi Ahilya university Indore, India, in 2009 and 2011, respectively.

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2024 - 20252

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Now showing 1 - 2 of 2
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    Enhancing cross-domain transferability of black-box adversarial attacks on speaker recognition systems using linearized backpropagation
    (Springer, 13-05-2024) Patel, Umang; Bhilare, Shruti; Hati, Avik; Bhilare, Shruti; Bhilare, Shruti; Bhilare, Shruti; Bhilare, Shruti; Bhilare, Shruti; DA-IICT, Gandhinagar; Patel, Umang (202021006)
    Speaker recognition system (SRS) serves as the gatekeeper for secure access, using the unique vocal characteristics of individuals for identification and verification. SRS can be found several biometric security applications such as in banks, autonomous cars, military, and smart devices. However, as technology advances, so do the threats to these models. With the rise of adversarial attacks, these models have been put to the test. Adversarial machine learning (AML) techniques have been utilized to exploit vulnerabilities in SRS, threatening their reliability and security. In this study, we concentrate on transferability in AML within the realm of SRS. Transferability refers to the capability of adversarial examples generated for one model to outsmart another model. Our research centers on enhancing the transferability of adversarial attacks in SRS. Our innovative approach involves strategically skipping non-linear activation functions during the backpropagation process to achieve this goal. The proposed method yields promising results in enhancing the transferability of adversarial examples across diverse SRS architectures, parameters, features, and datasets. To validate the effectiveness of our proposed method, we conduct an evaluation using the state-of-the-art FoolHD attack, an attack designed specifically for exploiting SRS. By implementing our method in various scenarios, including cross-architecture, cross-parameter, cross-feature, and cross-dataset settings, we demonstrate its resilience and versatility. To evaluate the performance of the proposed method in improving transferability, we have introduced three novel metrics:�enhanced transferability,�relative transferability, and�effort in enhancing transferability. Our experiments demonstrate a significant boost in the transferability of adversarial examples in SRS. This research contributes to the growing body of knowledge on AML for SRS and emphasizes the urgency of developing robust defenses to safeguard these critical biometric systems.
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    Robust Adversarial Defense: An Analysis on Use of Auto-Inpainting
    (Springer, 01-01-2025) Sharma, Shivam; Joshi, Rohan; Bhilare, Shruti; Joshi, Manjunath V; DA-IICT, Gandhinagar