WebAssembly (Wasm) is an emerging binary format that serves as a compilation target for over 40 programming languages. Wasm runtimes provide execution environments that enhance portability by abstracting away operatin...
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WebAssembly (Wasm) is an emerging binary format that serves as a compilation target for over 40 programming languages. Wasm runtimes provide execution environments that enhance portability by abstracting away operating systems and hardware details. A key component in these runtimes is the WebAssembly system Interface (WASI), which manages interactions with operating systems, like file operations. Considering the critical role of Wasm runtimes, the community has aimed to detect their implementation bugs. However, no work has focused on WASI-specific bugs that can affect the original functionalities of running Wasm binaries and cause unexpected results. To fill the void, we present DrWASI, the first general-purpose differential testing framework for WASI implementations. Our approach uses a large language model to generate seeds and applies variant and environment mutation strategies to expand and enrich the test case corpus. We then perform differential testing across major Wasm runtimes. By leveraging dynamic and static information collected during and after the execution, DrWASI can identify bugs. Our evaluation shows that DrWASI uncovered 33 unique bugs, with all confirmed and 7 fixed by developers. This research represents a pioneering step in exploring a promising yet under-explored area of the Wasm ecosystem, providing valuable insights for stakeholders.
Iterative inference approaches have shown promising success in the task of multi-view depth estimation. However, these methods put excessive emphasis on the universal inter-view correspondences while neglecting the co...
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Iterative inference approaches have shown promising success in the task of multi-view depth estimation. However, these methods put excessive emphasis on the universal inter-view correspondences while neglecting the correspondence ambiguity in regions of low texture and depth discontinuous areas. Thus, they are prone to produce inaccurate or even erroneous depth estimations, which is further exacerbated cumulative errors especially in the iterative pipeline, providing unreliable information in many real-world scenarios. In this paper, we revisit this issue from the intra-view Contextual Hints and introduce a novel enhancing iterative approach, named EnIter. Concretely, at the beginning of each iteration, we present a Depth Intercept (DI) modulator to provide more accurate depth by aggregating neighbor uncertainty, correlation volume of reference and normal. This plug and play modulator is effective at intercepting the erroneous depth estimations with implicit guidance from the universal correlation contextual hints, especially for the challenging regions. Furthermore, at the end of each iteration, we refine the depth map with another plug and play modulator termed as Depth Refine (DR). It mines the latent structure knowledge of reference Contextual Hints and establishes one-way dependency using local attention from reference features to depth, yielding delicate depth in details. Extensive experiment demonstrates that our method not only achieves state-of-the-art performance over existing models but also exhibits remarkable universality in popular iterative pipelines, e.g., CasMVS, UCSNet, TransMVS, UniMVS.
Artificial intelligence (AI) empowered edge computing has given rise to a new paradigm and effectively facilitated the promotion and development of multimedia applications. The speech assistant is one of the significa...
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Artificial intelligence (AI) empowered edge computing has given rise to a new paradigm and effectively facilitated the promotion and development of multimedia applications. The speech assistant is one of the significant services provided by multimedia applications, which aims to offer intelligent interactive experiences between humans and machines. However, malicious attackers may exploit spoofed speeches to deceive speech assistants, posing great challenges to the security of multimedia applications. The limited resources of multimedia terminal devices hinder their ability to effectively load speech spoofing detection models. Furthermore, processing and analyzing speech in the cloud can result in poor real-time performance and potential privacy risks. Existing speech spoofing detection methods rely heavily on annotated data and exhibit poor generalization capabilities for unseen spoofed speeches. To address these challenges, this paper first proposes the Coordinate Attention Network (CA2Net) that consists of coordinate attention blocks and Res2Net blocks. CA2Net can simultaneously extract temporal and spectral speech feature information and represent multi-scale speech features at a granularity level. Besides, a contrastive learning-based speech spoofing detection framework named GEMINI is proposed. GEMINI can be effectively deployed on edge nodes and autonomously learn speech features with strong generalization capabilities. GEMINI first performs data augmentation on speech signals and extracts conventional acoustic features to enhance the feature robustness. Subsequently, GEMINI utilizes the proposed CA2Net to further explore the discriminative speech features. Then, a tensor-based multi-attention comparison model is employed to maximize the consistency between speech contexts. GEMINI continuously updates CA2Net with contrastive learning, which enables CA2Net to effectively represent speech signals and accurately detect spoofed speeches. Extensive experiments on
WebAssembly (abbreviated as Wasm) was initially introduced for the Web and quickly extended its reach into various domains beyond the Web. To create Wasm applications, developers can compile high-level programming lan...
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WebAssembly (abbreviated as Wasm) was initially introduced for the Web and quickly extended its reach into various domains beyond the Web. To create Wasm applications, developers can compile high-level programming languages into Wasm binaries or manually write the textual format of Wasm and translate it into Wasm binaries by the toolchain. Regardless of whether it is utilized within or outside the Web, the execution of Wasm binaries is supported by the Wasm runtime. Such a runtime provides a secure, memory-efficient, and sandboxed execution environment to execute Wasm binaries. This paper provides a comprehensive survey of research on Wasm runtimes with 103 collected research papers related to Wasm runtimes following the traditional systematic literature review process. It characterizes existing studies from two different angles, including the internal research of Wasm runtimes (Wasm runtime design, testing, and analysis) and the external research (applying Wasm runtimes to various domains). This paper also proposes future research directions about Wasm runtimes.
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