Qiang Hu

Zitong Xu, Huiyu Duan, Xinyun Zhang et al.

Recent advances in unified multimodal models have significantly improved text-guided image editing abilities. In particular, models such as Nano-Banana-Pro and GPT-Image-2 demonstrate emerging capabilities in multi-source image editing (MIE), including tasks such as object synthesis, person-background composition, and cross-image style fusion. However, existing benchmarks and image editing assessment (IEQA) methods remain primarily focused on single-image editing tasks and largely overlook the more challenging setting of MIE. This highlights the urgent need for a comprehensive and human-aligned benchmark for MIE. To this end, we introduce MIE-Bench, the first large-scale multiple image editing benchmark with fine-grained human preference annotations. Specifically, MIE-Bench includes 3,000 editing instances across 16 tasks, each involving more than two source images and an editing prompt, together with 36K edited images produced by 12 state-of-the-art editing models and over 108K mean opinion scores (MOSs) covering visual quality, instruction following, and attribute preservation. Based on MIE-Bench, we propose MIEScore, a multimodal large language model (MLLM)-based evaluation model enhanced with skill optimization and multi-dimensional supervised fine-tuning, to provide human-aligned feedback for MIE. Extensive experiments show that MIEScore achieves state-of-the-art performance in aligning with human preferences and generalizes well across other IEQA datasets. Both the dataset and the model are available at https://github.com/IntMeGroup/MIEScore.

Hengxiang Zhang, Qiang Hu, Hongxin Wei

Over-parameterized models are typically vulnerable to membership inference attacks, which aim to determine whether a specific sample is included in the training of a given model. Previous Weight regularizations (e.g., L1 regularization) typically impose uniform penalties on all parameters, leading to a suboptimal tradeoff between model utility and privacy. In this work, we first show that only a small fraction of parameters substantially impact the privacy risk. In light of this, we propose Privacy-aware Sparsity Tuning (PAST), a simple fix to the L1 Regularization, by employing adaptive penalties to different parameters. Our key idea behind PAST is to promote sparsity in parameters that significantly contribute to privacy leakage. In particular, we construct the adaptive weight for each parameter based on its privacy sensitivity, i.e., the gradient of the loss gap with respect to the parameter. Using PAST, the network shrinks the loss gap between members and non-members, leading to strong resistance to privacy attacks. Extensive experiments demonstrate the superiority of PAST, achieving a state-of-the-art balance in the privacy-utility trade-off.

Weizhe Wang, Minhong Dong, Jinhao Li et al.

Network attackers often conceal malicious communication within legitimate Internet traffic. Existing CDN-based evasion techniques rely on SNI--Host inconsistency, insufficient domain ownership verification, or provider-specific routing rewrites, which limit their applicability in modern CDN environments. We identify a validation gap in DNS-based authorization, where permission derived from an allowed domain applies to a shared IP and can be reused to reach another tenant in both CDN and non-CDN shared-hosting environments. This paper presents the Domain Decoupling Attack (DDA), which resolves an allowed domain to obtain permission for a shared edge IP and subsequently connects to the same address while presenting the hidden domain consistently in both TLS SNI and HTTP Host. Measurements of 1,069,048 domains across six continents produce 18,025,068 successful probes and identify exposure rates of 95.8% overall, 99.26% for CDN domains, 92.75% for non-CDN domains, and 97.7% for non-CDN cross-tenant IPs, while laboratory experiments reveal a structural limitation of DNS-bound access control on shared addresses. These results clarify the security risks of DNS-derived IP authorization and support the evaluation and improvement of access-control mechanisms in CDN and non-CDN shared-hosting environments.