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
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.
文摘通过热压缩实验研究Ti-3.5Al-5Mo-6V-3Cr-2Sn-0.5Fe-0.1B-0.1C锻态合金在750~900℃温度范围和0.001~1 s^(-1)应变速率范围内的热变形特征。应力-应变曲线表明,流变应力随着温度的提高和应变速率的降低而降低。显微组织对热变形参数十分敏感,当应变速率保持在0.001 s^(-1)不变,温度达到790℃或者温度保持在910℃不变,应变速率不高于0.1s^(-1)时合金显微组织中出现动态再结晶晶粒。计算出加工硬化率θ,并发现动态再结晶倾向于在较高温度和较低应变速率条件下发生。得到合金的本构方程和加工硬化图,平均热激活能为242.78 k J/mol,而且只有少量失稳区在热加工图中出现,说明合金具有良好的热加工性能。当应变速率为0.1s^(-1)时,应力-应变曲线呈现一种异常的形状,连续出现两个应力峰值,这一现象可以归因于连续动态再结晶和再结晶晶粒的转动所造成的硬化现象与动态软化机制的混合作用。
