Privacy Protection in Deep Multi-modal Retrieval

Deep learning techniques have ushered in significant progress in large-scale multi-modal retrieval. Nevertheless, the advanced techniques may be used nefariously to conduct a search that violates the privacy of individuals. In this paper, we propose a novel PrIvacy Protection method (PIP) against malicious multi-modal retrieval models, which proactively transfers original data into adversarial data with quasi-imperceptible perturbations before releasing them. Consequently, unauthorized malicious parties are not able to use deployed deep models to find out desired sensitive information with them. In addition to privacy preserving, PIP synchronously learns an effective multi-modal retrieval model to facilitate authorized uses, endowed with strong resilience to the perturbations. To the best of our knowledge, it is a very first attempt to consider privacy issues in multi-modal retrieval, and encapsulate both privacy protection against unauthorized retrieval and robust multi-modal learning for authorized uses into a unified framework. This work is conducted in the challenging no-box and unsupervised settings, where neither target malicious models nor supervised information is known. The optimization objective of our versatile PIP is achieved through a two-player game between different components with both the intra- and inter-modality graph alignments and the domain distribution alignment considered. Besides, a high-level similarity matrix is developed to obtain reliable guidance for learning. Empirically, we apply the proposed PIP to hashing based multi-modal retrieval scenarios and prove its effectiveness on a range of benchmarks and tasks.