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Xiwen Wang

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5 papers

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AAAI Conference 2026 Conference Paper

SAR-DisentDM: A Semantic-Disentangled Diffusion Model for Limited-Data SAR Image Synthesis

Yue Yang
Song Tang
Qijun Zhao
Hailun Zhang
Xiwen Wang
Zijian Deng

The high cost of synthetic aperture radar (SAR) data acquisition motivates SAR image generation research. However, the data scarcity and SAR's inherent azimuth sensitivity make generative models suffer from severe azimuth overfitting. Most existing methods require supplementary data to work effectively, limiting their practicality. In this paper, we propose SAR-DisentDM, a novel semantic-disentangled diffusion model for limited-data SAR image generation, without requiring any auxiliary resources. We develop a physics-aware diffusion architecture that explicitly models semantic knowledge of SAR images, including intrinsic characteristics, contextual diversity, and measurement randomness. A key innovation is the attention-guided semantic disentanglement (AGSD) module, designed to decouple category-specific features from azimuth-variable scattering patterns. This is achieved by aid of a dual disentangled loss with time-step-adaptive optimization. Furthermore, we introduce an azimuth angle perturbation augmentation (AAPA) mechanism, to enhance the model's robustness to minor azimuth angle errors. Extensive evaluations validate that SAR-DisentDM enables controllable SAR image synthesis with designated attributes, significantly improving representation and generalization abilities under limited data. Synthetic imagery from our approach boosts automatic target recognition (ATR) accuracy beyond state-of-the-art methods.

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AAAI Conference 2025 Conference Paper

Mesoscopic Insights: Orchestrating Multi-Scale & Hybrid Architecture for Image Manipulation Localization

Xuekang Zhu
Xiaochen Ma
Lei Su
Zhuohang Jiang
Bo Du
Xiwen Wang
Zeyu Lei
Wentao Feng

The mesoscopic level serves as a bridge between the macroscopic and microscopic worlds, addressing gaps overlooked by both. Image manipulation localization (IML), a crucial technique to pursue truth from fake images, has long relied on low-level (microscopic-level) traces. However, in practice, most tampering aims to deceive the audience by altering image semantics. As a result, manipulation commonly occurs at the object level (macroscopic level), which is equally important as microscopic traces. Therefore, integrating these two levels into the mesoscopic level presents a new perspective for IML research. Inspired by this, our paper explores how to simultaneously construct mesoscopic representations of micro and macro information for IML and introduces the Mesorch architecture to orchestrate both. Specifically, this architecture i) combines Transformers and CNNs in parallel, with Transformers extracting macro information and CNNs capturing micro details, and ii) explores across different scales, assessing micro and macro information seamlessly. Additionally, based on the Mesorch architecture, the paper introduces two baseline models aimed at solving IML tasks through mesoscopic representation. Extensive experiments across four datasets have demonstrated that our models surpass the current state-of-the-art in terms of performance, computational complexity, and robustness.

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TCS Journal 2024 Journal Article

Updatable searchable symmetric encryption: Definitions and constructions

Xiwen Wang
Kai Zhang
Junqing Gong
Shi-Feng Sun
Jianting Ning

Searchable symmetric encryption (SSE) allows a client to search over encrypted data. To address the real threat of key compromise in practice, this work initiates the study of key rotation for SSE and introduces the notion of updatable SSE (USSE). In USSE, a client can issue a single update token that permits the server to convert existing encrypted data from the old key to the new key. In particular, • we formalize the syntax of USSE and define the security model that captures the inference of key, search token, update token and encrypted data with bi-/uni-/no-directional key updates and bi-/uni-directional encrypted data updates. • we present a USSE scheme that supports conjunctive queries with sub-linear complexity, and prove its security with no-directional key update and bi-directional encrypted data update. We also give extensions for concerning different key/encrypted data updates. • we implement our USSE schemes and evaluate the performance with real-world dataset, which illustrates that our schemes achieve practically acceptable computational overhead and communication cost. Technically, our formalization of USSE is inspired by updatable encryption (UE); our USSE schemes are obtained by a semi-generic transformation from Cash et al. 's SSE and UE. The transformation itself only relies on DL and DBDH assumptions. We believe that the transformation is of independent interest and applicable to other scenarios where the SSE systems follow the structure of Cash et al. 's work.

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NeurIPS Conference 2023 Conference Paper

Learning Large-Scale MTP$_2$ Gaussian Graphical Models via Bridge-Block Decomposition

Xiwen Wang
Jiaxi Ying
Daniel Palomar

This paper studies the problem of learning the large-scale Gaussian graphical models that are multivariate totally positive of order two ($\text{MTP}_2$). By introducing the concept of bridge, which commonly exists in large-scale sparse graphs, we show that the entire problem can be equivalently optimized through (1) several smaller-scaled sub-problems induced by a \emph{bridge-block decomposition} on the thresholded sample covariance graph and (2) a set of explicit solutions on entries corresponding to \emph{bridges}. From practical aspect, this simple and provable discipline can be applied to break down a large problem into small tractable ones, leading to enormous reduction on the computational complexity and substantial improvements for all existing algorithms. The synthetic and real-world experiments demonstrate that our proposed method presents a significant speed-up compared to the state-of-the-art benchmarks.

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AAAI Conference 2022 Conference Paper

Efficient Algorithms for General Isotone Optimization

Xiwen Wang
Jiaxi Ying
José Vinícius de M. Cardoso
Daniel P. Palomar

Monotonicity is often a fundamental assumption involved in the modeling of a number of real-world applications. From an optimization perspective, monotonicity is formulated as partial order constraints among the optimization variables, commonly known as isotone optimization. In this paper, we develop an efficient, provable convergent algorithm for solving isotone optimization problems. The proposed algorithm is general in the sense that it can handle any arbitrary isotonic constraints and a wide range of objective functions. We evaluate our algorithm and state-of-the-art methods with experiments involving both synthetic and realworld data. The experimental results demonstrate that our algorithm is more efficient by one to four orders of magnitude than the state-of-the-art methods.

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