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Jiageng Chen

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2 papers

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TIST Journal 2026 Journal Article

Adversarial Face Database against Deep Learning-Enabled Reconstruction Attacks

Hui Liu
Ling Ding
Jiageng Chen
Jinghua Wang
Xu Du
Jiabao Guo

Face recognition systems offer a range of applications that enhance security, efficiency, and personalization, e.g., access control, identity verification, and personalized services. Mainstream facial recognition systems employ the Edge-Cloud architecture to protect user privacy by storing facial feature data instead of original facial images. However, recently emerging reconstruction attacks based on deep learning can recover the visual information of original facial images from facial features, resulting in face privacy disclosure. Existing anti-reconstruction approaches either compromise facial recognition accuracy or fail to meet real-time requirements. In this article, we propose a practical privacy-preserving approach based on adversarial perturbations against reconstruction attacks. By incorporating subtle adversarial interference into facial features, the mapping relationship from facial features to original facial images is disrupted, and the baseline reconstruction networks cannot recover the original face image. We conducted experiments on two facial recognition models, FaceNet and ArcFace, both widely deployed in practical scenarios. The results show that the face recognition accuracy sacrifice of less than 1% can significantly reduce the quality of the reconstructed image. In terms of efficiency, the average time to generate an adversarial facial feature is less than 10 ms, meeting the real-time requirements of facial recognition.

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JBHI Journal 2025 Journal Article

A Lightweight Privacy-Preserving Federated Learning Framework for Heterogeneity-Resilient Skin Cancer Diagnosis

Junyu Lin
Jiageng Chen
Jichao Xiong
Dian Jiao
Weizhong Zhao
Yang Xiang

Machine Learning (ML) demonstrates dermatologist-level accuracy in skin cancer diagnosis, yet its practical adoption is constrained by data silos and privacy issues. While Federated Learning (FL) addresses these limitations, it remains susceptible to data heterogeneity and gradient leakage attacks. To overcome these challenges, we introduce a privacy-preserving FL framework tailored for encrypted dermoscopic image analysis. Our proposed framework integrates a Fully Homomorphic Encryption (FHE)-enabled variant of Stochastic Controlled Averaging (SCA), enhancing model convergence with Non-IID data. To further minimize computational and communication overhead, we develop a layer-wise Packed FHE (PFHE) approach that improves the efficiency of encrypted model aggregation. Moreover, we design a lightweight, FHE-Friendly Deep Neural Network (DNN) optimized for encrypted inference. This architecture incorporates a DO-EncConv module specifically engineered to balance inference efficiency and precision within FHE computational constraints. Experimental results on the HAM10000 and ISIC2019 datasets confirm the effectiveness of our proposed framework, demonstrating F1-Score improvements of 2. 2% and 4. 0%, respectively, over baseline FL approaches. Additionally, our method achieves communication overhead reductions of 94. 85% and 93. 48%, while encrypted inference is performed in approximately 17. 8 seconds per sample, with less than 2% accuracy degradation compared to centralized plaintext models. These outcomes underscore the framework's practicality and effectiveness for secure, scalable clinical deployment.

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