Weakly supervised Referring Expression Grounding (WREG) aims at grounding the target region based on a given expression, where the mapping between regions and expressions is unknown during training. Recent WREG methods leverage the strategy of generating pseudo-labels utilizing Vision-Language Pre-training (VLP) to avoid the cross-modal heterogeneous gaps arising from the two-stage reconstruction strategy. However, mainstream VLPs are trained with image-text alignment data, which makes the generated labels inapplicable to REG task. Furthermore, due to the constraints of WREG data, it is challenging to ensure the quality of the pseudo-labels. To this end, we propose a Cyclic Pseudo-label Generation and Refinement (CPGR) method to alleviate the above limitations. Specifically, we cycle through the process of Generation-Refinement-Grounding to alleviate the impact of missing region annotations. We perform REG task-adaptive fine-tuning on BLIP-2 to generate REG-style descriptions with Region-Centrality. Then, we design a Pseudo-label Refinement module by utilizing cross-modal token attention to enhance the reliability of pseudo-labels and ensure their Reference-Discrimination. Experiments on five benchmark datasets demonstrate that our proposed method outperforms the current state-of-the-art weakly supervised methods. Our code and models will be released at https://github.com/5jiahe/CPGR.

Anonymous Payment Channel Hub (PCH), one of the most promising layer-two solutions, settles the scalability issue in blockchain while guaranteeing the unlinkability of transacting parties. However, such developments bring conflicting requirements, i.e., hiding the sender-to-receiver relationships from any third party but opening the relationship to the auditor. Existing works do not support these requirements simultaneously since off-chain transactions are not recorded in the blockchain. Further, the privacy protection strategies hinder auditors from capturing the payment relationships. Thus, it is still a challenge to audit the finance activities of PCH transacting parties. This paper proposes a novel anonymous PCH solution called AuditPCH to achieve privacy and auditability. Concretely, we design a Linkable Randomizable Puzzle scheme for constructing conditional transactions, allowing a sender to pay for a receiver via the hub. As such, AuditPCH, with the new LRP scheme, ensures that 1) payment relationships can be protected from the hub and 2) an auditor with necessary trapdoors can associate the sender and receiver of a payment. We prove the security of AuditPCH under the Global Universal Composability framework. The extensive experimental evaluations on AuditPCH are established to demonstrate its functionality and flexibility.

Weakly supervised Referring Expression Grounding (REG) aims to localize the target entity in an image based on a given expression, where the mapping between image regions and expressions is unknown during training. It faces two primary challenges. Firstly, conventional methods involve selecting regions to generate reconstructed texts for computing the backpropagation loss between regions and expressions. However, semantic deviations in text reconstruction may result in significant cross-modal bias, leading to substantial losses even in cases of correctly matched regions. Secondly, the absence of region-level ground truth in weakly supervised REG results in a lack of stable and reliable supervision during training. To tackle these challenges, we propose a Progressive Semantic Reconstruction Network (PSRN), which utilizes a two-level matching-reconstruction process based on the key triad and adaptive phrases, respectively. We leverage progressive semantic reconstruction with a three-staged training strategy to mitigate the deviations in the reconstructed texts. Additionally, we introduce a Constrained Interactions operation and an Attention Coordination mechanism to facilitate additional bidirectional supervision between the two matching processes. Experiments on three benchmark datasets of RefCOCO, RefCOCO+ and RefCOCOg demonstrate that the proposed PSRN has the competing results. Our source code will be released athttps://github.com/5jiahe/psrn.

Tor is a relay-based communication network that provides users with anonymous access to the Internet. This paper proposes a new attack to associate the anonymous communication relationship between the client and the server in the Tor network, which exploits defects of the Tor protocol, resulting in deanonymizing the Tor network. In this attack, a malicious entry onion router modifies the command field of cell sequences derived from the client to embed a signal sequence. While the exit onion router detects a signal sequence consistent with the embedded signal sequence, the anonymous communication relationships between the client and the server can be confirmed. We have implemented the attack in a private Tor network and our experiments validate its feasibility and effectiveness, with 100% accuracy, 97.5 % true positive rate, and zero false positive rate.