Deep learning-based video steganography has made significant strides, yet conventional explicit methods often suffer from cover distortion and reduced extraction accuracy at high capacities. In this paper, we propose an implicit video steganography framework that treats video hiding and recovery as a dual-stream generation process leveraging implicit neural representations. Instead of altering existing carriers, secret information is encoded within the neural network’s weights, making it an inherent part of the generation process. We introduce a dual-stream input encoding mechanism that decouples the input space into temporal and cryptographic encodings to ensure covert transmission, allowing only authorized receivers to recover hidden content. Furthermore, a multi-scale generation network, incorporating frequency-aware upscaling and statistical distribution loss, is presented to achieve high-quality reconstruction. Extensive experiments demonstrate that our approach achieves state-of-the-art results, minimizing detectable discrepancies while concealing up to seven secret videos within a single carrier. Our method significantly outperforms existing benchmarks by a margin of over 10 dB in peak signal-to-noise ratio, highlighting its superior imperceptibility, accuracy, and security.
Yifei Wang, Gaozhi Liu, Sheng Li et al.· Computer/law journal· 0 citations
Complex tasks often decompose into parallelizable yet interdependent subtasks, making orchestration critical to the performance of multi-agent systems (MAS). Existing evaluations typically rely on end-to-end execution, which conflates orchestration-plan quality with worker capabilities, tool reliability, and environmental noise. Moreover, the time and token costs of real execution grow rapidly with workflow scale, making systematic evaluation expensive. We present OrchBench, a simulation-based benchmark for evaluating multi-agent orchestration plans in isolation. Starting from real-world tasks, OrchBench constructs directed acyclic graphs (DAGs) that encode task dependencies, with controlled sizes and degrees of parallelism. Given a DAG, a per-agent context limit, and an agent budget, the evaluated planner assigns subtasks to agents and specifies cross-agent information transfers and their retention ratios. A deterministic simulator evaluates the resulting plan without invoking worker agents and returns interpretable measures of result quality, makespan, and token cost. The simulated scores produced by OrchBench correlate strongly with quality scores from Claude Code executions, achieving a Pearson correlation of \(r=0.816\), while requiring only \(1.3\%\) of the tokens and \(10.3\%\) of the wall-clock time. Across diverse planners and workflow scales, we find that preserving task-critical information is more important than simply increasing the number of agents, and the benefits of parallelism diminish as coordination failures accumulate. These results establish OrchBench as an efficient and interpretable benchmark for comparing and diagnosing multi-agent orchestration plans.
