{"ID":2827363,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2512.16123","arxiv_id":"2512.16123","title":"Autoencoder-based Denoising Defense against Adversarial Attacks on Object Detection","abstract":"Deep learning-based object detection models play a critical role in real-world applications such as autonomous driving and security surveillance systems, yet they remain vulnerable to adversarial examples. In this work, we propose an autoencoder-based denoising defense to recover object detection performance degraded by adversarial perturbations. We conduct adversarial attacks using Perlin noise on vehicle-related images from the COCO dataset, apply a single-layer convolutional autoencoder to remove the perturbations, and evaluate detection performance using YOLOv5. Our experiments demonstrate that adversarial attacks reduce bbox mAP from 0.2890 to 0.1640, representing a 43.3% performance degradation. After applying the proposed autoencoder defense, bbox mAP improves to 0.1700 (3.7% recovery) and bbox mAP@50 increases from 0.2780 to 0.3080 (10.8% improvement). These results indicate that autoencoder-based denoising can provide partial defense against adversarial attacks without requiring model retraining.","short_abstract":"Deep learning-based object detection models play a critical role in real-world applications such as autonomous driving and security surveillance systems, yet they remain vulnerable to adversarial examples. In this work, we propose an autoencoder-based denoising defense to recover object detection performance degraded b...","url_abs":"https://arxiv.org/abs/2512.16123","url_pdf":"https://arxiv.org/pdf/2512.16123v1","authors":"[\"Min Geun Song\",\"Gang Min Kim\",\"Woonmin Kim\",\"Yongsik Kim\",\"Jeonghyun Sim\",\"Sangbeom Park\",\"Huy Kang Kim\"]","published":"2025-12-18T03:19:40Z","proceeding":"cs.CR","tasks":"[\"cs.CR\",\"cs.AI\",\"cs.CV\"]","methods":"[]","has_code":false}