Cloud-based video analytics services have been widely employed to support various real-world surveillance and monitoring applications while bearing the risk of disclosing sensitive visuals. The state-of-the-art (SoTA) solution has explored the feasibility of applying cryptographic techniques for privacy-preserving video analytics, but unfortunately incurs high computation and communication burdens on the end users. In this article, we propose Pri3D , an efficient privacy-preserving video analytics system performed over two distributed clouds. Pri3D flexibly combines additive and multiplicative secret sharing techniques to free end devices and facilitate on-premise analytics services. Particularly, targeting the mainstream 3D convolutional neural network (CNN) pipeline, Pri3D securely accomplishes the non-linear operations (e.g., ReLU and max pooling) in merely two interaction rounds, owing to the novel design of the bi-directional transforming protocols for different modalities of secret sharing. To further optimize the latency and bandwidth confronted with large amounts of video data, \(\textsf{AS2MS}^{++}\) and \(\textsf{MS2AS}^{++}\) are proposed by subtly utilizing randomization factors and pre-encrypted nonce. With the transforming protocols, a series of privacy-preserving layer protocols are devised and tailored to build up the privacy-preserving analytics pipeline. Theoretical analysis shows that Pri3D can effectively fulfill the desired privacy requirements. Extensive evaluations demonstrate that Pri3D provides up to 11.85 \(\times\) speed boost and 14.86 \(\times\) communication reduction compared to the SoTA work, while it is sufficiently efficient for working on resource-constrained devices.

The task of few-shot classification (FSC) is to build a model to discriminate novel categories that do not present in training categories with limited labeled samples. However, in many applications, the training categories and novel categories are assumed to come from the same domain. Existing few-shot classification algorithms achieve promising performance in a single domain, but often fail to generalize to unseen domains because of the different feature distribution across domains. The main goal of this work is to propose an effective recognition model that can work on various image domains with domain shift. Specifically, we propose to construct a new domain-attention mechanism with some adapters based on the squeeze-and-excitation network architectures . The proposed network processes multiple domains simultaneously and all parameters are shared across domains. In addition, because of the large discrepancy in feature distributions among different domains, similarity transformation layers are used to reduce the differences in the feature distributions in the training stage. Extensive experiments were conducted to validate the domain generalization capability (GC) of this model on four FSC datasets: mini-ImageNet, Cars, CUB-200-2011, and Simpsons Characters Data. The results show that the presented method has excellent performances on various datasets across diverse domains.

Due to the immense benefits of guaranteeing user privacy, popular messaging platforms have shown enthusiasm for deploying End-to-End Encryption (E2EE). However, E2EE could be misused for bypassing media moderation, opening a shortcut for the viral spreading of harmful media. Private hash-matching techniques are proposed to identify harmful content in E2EE. Unfortunately, the pioneering solution incurs prohibitively high latency due to redundant user-cloud interactions for a private inspection. In this paper, we designEntbergenfor efficient inspection of E2EE media by differentially handling harmless and harmful ingredients. For this, a novel Private-2D BloOm filter with Fuzzy Query (PBO-FQ) is designed for local, agile, and private media hash matching. It is proposed as the first structure that adapts inverted index and differential privacy (DP) towards seamless integration of sketch and mask encoding. With PBO-FQ,Entbergencan instantly filter out harmless media and only pays attention to the small-scale counterparts by scrutinizing them privately based on homomorphic encryption. Security analysis shows thatEntbergencan effectively fulfil the desired privacy requirements. Extensive evaluations demonstrate thatEntbergenis sufficiently efficient (w.r.t. computation and communication overhead) for working on mobile devices and can easily scale to real-world inspection with a large database.

Photo capturing and sharing have become routine daily activities for social platform users. Alongside the entertainment of social interaction, we are experiencing tremendous visual violation and photo abusing. Especially, users may be unconsciously filmed and exposed online, which is termed as the non-consensual sharing issue. Unfortunately, this problem cannot be well handled with proactive access control or dedicated bystander detection, as users are unaware of their situations and may be filmed stealthily. We propose Videre on behalf of the privacy of the unaware parties in a way that they would be automatically identified and warned before such photos go public. For this, we first elaborate on the predominant features encountered in non-consensual captured photos via a thorough user study. Then we establish a dataset for this context and build a classifier as a proactive detector based on multi-deep-feature fusion. To relieve the burden of person-wise unawareness detection, we further design a signature-based filter for local pre-authorization, which can also implicitly avoid classification errors. We implement and test Videre in various field settings to demonstrate its effectiveness and performance.