The object detectors can precisely detect the camouflaged object beyond human perception.The investigations reveal that the CNNs-based(Convolution Neural Networks)detectors are vulnerable to adversarial attacks.Some w...The object detectors can precisely detect the camouflaged object beyond human perception.The investigations reveal that the CNNs-based(Convolution Neural Networks)detectors are vulnerable to adversarial attacks.Some works can fool detectors by crafting the adversarial camouflage attached to the object,leading to wrong prediction.It is hard for military operations to utilize the existing adversarial camouflage due to its conspicuous appearance.Motivated by this,this paper proposes the Dual Attribute Adversarial Camouflage(DAAC)for evading the detection by both detectors and humans.Generating DAAC includes two steps:(1)Extracting features from a specific type of scene to generate individual soldier digital camouflage;(2)Attaching the adversarial patch with scene features constraint to the individual soldier digital camouflage to generate the adversarial attribute of DAAC.The visual effects of the individual soldier digital camouflage and the adversarial patch will be improved after integrating with the scene features.Experiment results show that objects camouflaged by DAAC are well integrated with background and achieve visual concealment while remaining effective in fooling object detectors,thus evading the detections by both detectors and humans in the digital domain.This work can serve as the reference for crafting the adversarial camouflage in the physical world.展开更多
Accurate segmentation of camouflage objects in aerial imagery is vital for improving the efficiency of UAV-based reconnaissance and rescue missions.However,camouflage object segmentation is increasingly challenging du...Accurate segmentation of camouflage objects in aerial imagery is vital for improving the efficiency of UAV-based reconnaissance and rescue missions.However,camouflage object segmentation is increasingly challenging due to advances in both camouflage materials and biological mimicry.Although multispectral-RGB based technology shows promise,conventional dual-aperture multispectral-RGB imaging systems are constrained by imprecise and time-consuming registration and fusion across different modalities,limiting their performance.Here,we propose the Reconstructed Multispectral-RGB Fusion Network(RMRF-Net),which reconstructs RGB images into multispectral ones,enabling efficient multimodal segmentation using only an RGB camera.Specifically,RMRF-Net employs a divergentsimilarity feature correction strategy to minimize reconstruction errors and includes an efficient boundary-aware decoder to enhance object contours.Notably,we establish the first real-world aerial multispectral-RGB semantic segmentation of camouflage objects dataset,including 11 object categories.Experimental results demonstrate that RMRF-Net outperforms existing methods,achieving 17.38 FPS on the NVIDIA Jetson AGX Orin,with only a 0.96%drop in mIoU compared to the RTX 3090,showing its practical applicability in multimodal remote sensing.展开更多
伪装目标与背景具有高度的相似性,极易受背景特征混淆,导致边界信息难以分辨且提取目标特征困难。目前主流的伪装目标检测(COD)算法主要针对性研究伪装目标本身及其边界行,忽略了图像背景与目标的相互关系,在复杂场景下的检测结果不理...伪装目标与背景具有高度的相似性,极易受背景特征混淆,导致边界信息难以分辨且提取目标特征困难。目前主流的伪装目标检测(COD)算法主要针对性研究伪装目标本身及其边界行,忽略了图像背景与目标的相互关系,在复杂场景下的检测结果不理想。为了探索背景和目标的潜在联系,提出一种通过挖掘边界和背景检测伪装目标的算法——I2DNet(Indirect to Direct Network)。该算法由5个部分组成:编码器,处理初始原始数据;边界指导的特征提取和挖掘框架,通过特征处理和特征挖掘提取更多精细的边界特征;背景引导的潜在特征学习框架,通过多尺度卷积探索更多的显著特征,同时基于注意力设计混合注意力模块(HAM),增强对背景特征的强化选择;信息补偿模块(ISM),弥补在特征处理过程中损失的细节信息;多任务协同分割解码器(MCD)则高效融合不同任务和模块提取的特征,并输出最终的预测结果。在广泛使用的3个数据集上的实验结果表明,所提算法优于其他15个先进模型,尤其在CAMO数据集上的平均绝对误差指标下降至0.042。展开更多
基金National Natural Science Foundation of China(grant number 61801512,grant number 62071484)Natural Science Foundation of Jiangsu Province(grant number BK20180080)to provide fund for conducting experiments。
文摘The object detectors can precisely detect the camouflaged object beyond human perception.The investigations reveal that the CNNs-based(Convolution Neural Networks)detectors are vulnerable to adversarial attacks.Some works can fool detectors by crafting the adversarial camouflage attached to the object,leading to wrong prediction.It is hard for military operations to utilize the existing adversarial camouflage due to its conspicuous appearance.Motivated by this,this paper proposes the Dual Attribute Adversarial Camouflage(DAAC)for evading the detection by both detectors and humans.Generating DAAC includes two steps:(1)Extracting features from a specific type of scene to generate individual soldier digital camouflage;(2)Attaching the adversarial patch with scene features constraint to the individual soldier digital camouflage to generate the adversarial attribute of DAAC.The visual effects of the individual soldier digital camouflage and the adversarial patch will be improved after integrating with the scene features.Experiment results show that objects camouflaged by DAAC are well integrated with background and achieve visual concealment while remaining effective in fooling object detectors,thus evading the detections by both detectors and humans in the digital domain.This work can serve as the reference for crafting the adversarial camouflage in the physical world.
基金National Natural Science Foundation of China(Grant Nos.62005049 and 62072110)Natural Science Foundation of Fujian Province(Grant No.2020J01451).
文摘Accurate segmentation of camouflage objects in aerial imagery is vital for improving the efficiency of UAV-based reconnaissance and rescue missions.However,camouflage object segmentation is increasingly challenging due to advances in both camouflage materials and biological mimicry.Although multispectral-RGB based technology shows promise,conventional dual-aperture multispectral-RGB imaging systems are constrained by imprecise and time-consuming registration and fusion across different modalities,limiting their performance.Here,we propose the Reconstructed Multispectral-RGB Fusion Network(RMRF-Net),which reconstructs RGB images into multispectral ones,enabling efficient multimodal segmentation using only an RGB camera.Specifically,RMRF-Net employs a divergentsimilarity feature correction strategy to minimize reconstruction errors and includes an efficient boundary-aware decoder to enhance object contours.Notably,we establish the first real-world aerial multispectral-RGB semantic segmentation of camouflage objects dataset,including 11 object categories.Experimental results demonstrate that RMRF-Net outperforms existing methods,achieving 17.38 FPS on the NVIDIA Jetson AGX Orin,with only a 0.96%drop in mIoU compared to the RTX 3090,showing its practical applicability in multimodal remote sensing.
文摘伪装目标与背景具有高度的相似性,极易受背景特征混淆,导致边界信息难以分辨且提取目标特征困难。目前主流的伪装目标检测(COD)算法主要针对性研究伪装目标本身及其边界行,忽略了图像背景与目标的相互关系,在复杂场景下的检测结果不理想。为了探索背景和目标的潜在联系,提出一种通过挖掘边界和背景检测伪装目标的算法——I2DNet(Indirect to Direct Network)。该算法由5个部分组成:编码器,处理初始原始数据;边界指导的特征提取和挖掘框架,通过特征处理和特征挖掘提取更多精细的边界特征;背景引导的潜在特征学习框架,通过多尺度卷积探索更多的显著特征,同时基于注意力设计混合注意力模块(HAM),增强对背景特征的强化选择;信息补偿模块(ISM),弥补在特征处理过程中损失的细节信息;多任务协同分割解码器(MCD)则高效融合不同任务和模块提取的特征,并输出最终的预测结果。在广泛使用的3个数据集上的实验结果表明,所提算法优于其他15个先进模型,尤其在CAMO数据集上的平均绝对误差指标下降至0.042。
