Adversarial training is an effective approach to enhance the robustness of machine learning models via adding adversarial examples into the training phase. However, existing adversarial training methods increase the advantage of membership inference attacks, which aim to determine from the model whether an example is in the training dataset. In this article, we propose an adversarial training framework that guarantees both robustness and membership privacy by introducing a tailor-made example called reverse-symmetry example. Moreover, our framework reduces the number of required adversarial examples compared with existing adversarial training methods. We implement our framework using four adversarial training methods on the FMNIST and CIFAR10 datasets and compare its performance with deep learning differential privacy. Our experimental findings demonstrate that our framework mitigates model overfitting and outperforms the original adversarial training with respect to the overall performance of accuracy, robustness, privacy, and runtime.

Biometric authentication schemes, i.e., fingerprint and face authentication, raise serious privacy concerns. To alleviate such concerns, hand authentication has been proposed recently. Existing hand authentication schemes, however, use dedicated hardware, such as infrared or depth cameras, which are not available on commodity mobile devices. In this paper, we presentEchoHand, a high accuracy and presentation attack resistant authentication scheme that complements camera-based 2-dimensional hand geometry recognition of one hand with an active acoustic sensing of the other hand. To this end,EchoHandplays an inaudible acoustic signal using the speaker to actively sense the holding hand and collects the echoes using the microphone.EchoHanddoes not rely on any specialized hardware but uses the built-in speaker, microphone and camera.EchoHanddoes not place more burdens on users than existing hand authentication methods. We conduct comprehensive experiments to evaluate the reliability, security, and usability ofEchoHand. The results show thatEchoHandhas a low equal error rate of 2.45% with as few as 10 training data points and it defeats presentation attacks. The results of the user study also suggest that the required hand gestures are easy to perform, andEchoHandis very user-friendly with low latency.

As smartphones proliferate, secure and user-friendly authentication methods are increasingly critical. Existing behavioral biometrics, however, are often compromised by behavior variability, leading to poor authentication accuracy and an unsatisfactory user experience. To fill this gap, we proposeBioHold, a new robust and reliable user authentication method, fusing finger behavior and hand geometry, captured via a smartphone's multitouch screen during natural holding gestures. It synergistically fuses behavioral and physiological biometrics. In contrast to traditional methods that require restrictive, unnatural user patterns, our approach utilizes a stable, natural gesture for authentication, effectively mitigating behavior variability. It enables one-handed authentication through familiar smartphone-holding and unlocking gestures. During this interaction, hand geometry and behavioral characteristics are recorded for subsequent authentication. We evaluate our method using a dataset collected from 20 subjects, demonstrating its resilience against behavioral variability over time while maintaining a high level of distinctiveness. With only 10 training samples, our method achieves an equal error rate of 3.59%, which improves to 1.25% with 40 training samples. Importantly, our method is resistant to common security threats such as zero-effort attacks, smudge attacks, and shoulder surfing attacks. A usability study confirms the method's high user acceptance, as measured by the system usability score.