IACD: Iterative Adversarial Collaborative Detection via Dual-Perspective Blind Spot Discovery

Modern object detectors systematically fail on concealed, camouflaged, or strategically hidden objects, not due to insufficient capacity, but because they reason solely from visual appearance with no mechanism to consider where objects might be hidden. We propose IACD (Iterative Adversarial Collaborative Detection), a dual-perspective framework that pairs a frozen pretrained YOLOv11m (Seeker) with a lightweight trainable module (Hider) that reasons inversely: given the environment, where would a target be hidden? The Hider analyzes environmental concealment suitability across texture, edge, and semantic dimensions, then identifies the Seeker’s blind spots by contrasting suitability against detection coverage. The resulting blind spot maps residually amplify feature responses via attention gates before a second detection pass. The two branches interact across multiple rounds, with the Hider’s blind spot estimates informing the attention gates that guide the second detection pass. Crucially, the Hider is trained under weak supervision derived solely from the detector’s own false negatives, requiring no manual concealment annotations. We frame this as detector blind-region recovery—recovering a frozen detector’s false negatives—rather than as standard camouflaged object detection. Experiments on the Poppy illegal crop detection dataset and the VisDrone aerial surveillance benchmark demonstrate consistent improvements over the strong YOLOv11m baseline, while introducing only ∼9M trainable parameters atop the frozen detector.