Dynamic deep neural networks, particularly multi-exit networks, are increasingly recognized for their efficiency in edge-cloud scenarios. However, they are vulnerable to latency attacks that can degrade performance by increasing computation time. Current attack strategies often require white-box access to the model or lead to significant drops in inference accuracy, making them easily detectable. This paper introduces SPLAT, a novel approach for executing stealthy and practical latency attacks on dynamic multi-exit models under black-box conditions. SPLAT employs a two-stage mechanism: the first stage generates coarse-grained attack inputs using a functional surrogate model, while the second stage refines these perturbations through an efficient query strategy to enhance stealthiness and effectiveness. Extensive experiments validate that SPLAT significantly outperforms existing methods across various models and datasets.

Deep Reinforcement Learning (DRL) based navigation methods have demonstrated promising results for mobile robots, but suffer from limited action flexibility in confined spaces. Conventional DRL approaches predominantly learn forward-motion policies, causing robots to become trapped in complex environments where backward maneuvers are necessary for recovery. This paper presents MAER-Nav (Mirror-Augmented Experience Replay for Robot Navigation), a novel framework that enables bidirectional motion learning without requiring explicit failure-driven hindsight experience replay or reward function modifications. Our approach integrates a mirror-augmented experience replay mechanism with curriculum learning to generate synthetic backward navigation experiences from successful trajectories. Experimental results in both simulation and real-world environments demonstrate that MAER-Nav significantly outperforms state-of-the-art methods while maintaining strong forward navigation capabilities. The framework effectively bridges the gap between the comprehensive action space utilization of traditional planning methods and the environmental adaptability of learning-based approaches, enabling robust navigation in scenarios where conventional DRL methods consistently fail.