Learning Without Seeing: Privacy-Preserving and Adversarial Perspectives in Lensless Imaging

Conventional computer vision relies on spatially resolved, human-interpretable images, which inherently expose sensitive information and raise privacy concerns. In this study, we explore an alternative paradigm based on lensless imaging, where scenes are captured as diffraction patterns governed by the point spread function (PSF). Although unintelligible to humans, these measurements encode structured, distributed information that remains useful for computational inference. 

We propose a unified framework for privacy-preserving vision that operates directly on lensless sensor measurements by leveraging their frequency-domain and phase-encoded properties. The framework is developed along two complementary directions. First, we enable reconstruction-free inference by exploiting the intrinsic obfuscation of lensless data. We show that semantic tasks such as classification can be performed directly on diffraction patterns using models tailored to non-local, phase-scrambled representations. We further design lensless-aware architectures and integrate them into practical pipelines, including a Swin Transformer-based steganographic framework (DiffHide) for secure and imperceptible information embedding. To assess robustness, we formalize adversarial threat models and develop defenses against learning-based reconstruction attacks, particularly GAN-driven inversion. Second, we investigate the limits of privacy by studying the reconstructability of lensless measurements without explicit knowledge of the forward model. We develop learning-based reconstruction methods that approximate the inverse mapping and analyze conditions under which sensitive information can be recovered. Our results demonstrate that lensless measurements enable effective vision tasks without reconstruction, while providing a principled framework to evaluate and mitigate privacy risks.