Advances in Adversarial Attacks and Object Detection

The field of object detection and adversarial attacks is rapidly evolving, with a focus on improving the robustness and efficiency of detection systems. Recent developments have led to the creation of more effective adversarial patch attacks, which can deceive dual-modal detectors across various scales, views, and scenarios. Additionally, innovations in remote sensing technology have enabled the development of more accurate crop detection methods, which can detect small targets in complex environments. Furthermore, advancements in cache attack techniques have reduced the time required to generate eviction sets, making them more practical for real-world applications. Researchers have also explored the use of multi-modal fusion and attention mechanisms to enhance small target detection in military applications. Noteworthy papers include: CDUPatch, which proposes a universal cross-modal patch attack against visible-infrared object detectors. YOLO-RS, which introduces a novel target detection model that enhances the detection of small targets in remote sensing images. Slice+Slice Baby, which presents a method for generating last-level cache eviction sets in a significantly shorter amount of time compared to state-of-the-art techniques.

Sources

CDUPatch: Color-Driven Universal Adversarial Patch Attack for Dual-Modal Visible-Infrared Detectors

YOLO-RS: Remote Sensing Enhanced Crop Detection Methods

Slice+Slice Baby: Generating Last-Level Cache Eviction Sets in the Blink of an Eye

Enhanced Small Target Detection via Multi-Modal Fusion and Attention Mechanisms: A YOLOv5 Approach

HeatSense: Intelligent Thermal Anomaly Detection for Securing NoC-Enabled MPSoCs

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