Futai Zou

Yihao Zou, Tianming Zheng, Futai Zou et al.

Fuzzing has become a widely adopted technique for vulnerability discovery, yet it remains ineffective for structured-input programs due to strict syntactic constraints and limited semantic awareness. Traditional greybox fuzzers rely on mutation-based strategies and coarse-grained coverage feedback, which often fail to generate valid inputs and explore deep execution paths. Recent advances in large language models (LLMs) have shown promise in improving input generation, but existing approaches primarily focus on seed generation and largely overlook the effective use of runtime feedback. In this paper, we propose SDLLMFuzz, a dynamic-static LLM-assisted greybox fuzzing framework for structured-input programs. Our approach integrates LLM-based structure-aware seed generation with static crash analysis, forming a unified feedback loop that iteratively refines test inputs. Specifically, we leverage LLMs to generate syntactically valid and semantically diverse inputs, while extracting rich semantic information from crash artifacts (e.g., core dumps and execution traces) to guide subsequent input generation. This dynamic-static feedback mechanism enables more efficient exploration of complex program behaviors. We evaluate SDLLMFuzz on the Magma benchmark across multiple structured-input programs, including libxml2, libpng, and libsndfile. Experimental results show that SDLLMFuzz significantly outperforms traditional greybox fuzzers and LLM-assisted baselines in terms of bug discovery and time-to-bug. These results demonstrate that combining semantic input generation with feedback-driven refinement is an effective direction for improving fuzzing performance on structured-input programs.

Chengwei Zhang, Yunlu Xu, Zhanzhan Cheng et al.

Arbitrary text appearance poses a great challenge in scene text recognition tasks. Existing works mostly handle with the problem in consideration of the shape distortion, including perspective distortions, line curvature or other style variations. Therefore, methods based on spatial transformers are extensively studied. However, chromatic difficulties in complex scenes have not been paid much attention on. In this work, we introduce a new learnable geometric-unrelated module, the Structure-Preserving Inner Offset Network (SPIN), which allows the color manipulation of source data within the network. This differentiable module can be inserted before any recognition architecture to ease the downstream tasks, giving neural networks the ability to actively transform input intensity rather than the existing spatial rectification. It can also serve as a complementary module to known spatial transformations and work in both independent and collaborative ways with them. Extensive experiments show that the use of SPIN results in a significant improvement on multiple text recognition benchmarks compared to the state-of-the-arts.

Chengwei Zhang, Yunlu Xu, Zhanzhan Cheng et al.

Temporal action localization is an important yet challenging research topic due to its various applications. Since the frame-level or segment-level annotations of untrimmed videos require amounts of labor expenditure, studies on the weakly-supervised action detection have been springing up. However, most of existing frameworks rely on Class Activation Sequence (CAS) to localize actions by minimizing the video-level classification loss, which exploits the most discriminative parts of actions but ignores the minor regions. In this paper, we propose a novel weakly-supervised framework by adversarial learning of two modules for eliminating such demerits. Specifically, the first module is designed as a well-designed Seeded Sequence Growing (SSG) Network for progressively extending seed regions (namely the highly reliable regions initialized by a CAS-based framework) to their expected boundaries. The second module is a specific classifier for mining trivial or incomplete action regions, which is trained on the shared features after erasing the seeded regions activated by SSG. In this way, a whole network composed of these two modules can be trained in an adversarial manner. The goal of the adversary is to mine features that are difficult for the action classifier. That is, erasion from SSG will force the classifier to discover minor or even new action regions on the input feature sequence, and the classifier will drive the seeds to grow, alternately. At last, we could obtain the action locations and categories from the well-trained SSG and the classifier. Extensive experiments on two public benchmarks THUMOS'14 and ActivityNet1.3 demonstrate the impressive performance of our proposed method compared with the state-of-the-arts.