An Active Deception Defense Model Based on Address Mutation and Fingerprint Camouflage

The static and predictable characteristics of cyber systems give attackers an asymmetric advantage in gathering useful information and launching attacks.To reverse this asymmetric advantage,a new defense idea,called Moving Target Defense(MTD),has been proposed to provide additional selectable measures to complement traditional defense.However,MTD is unable to defeat the sophisticated attacker with fingerprint tracking ability.To overcome this limitation,we go one step beyond and show that the combination of MTD and Deception-based Cyber Defense(DCD)can achieve higher performance than either of them.In particular,we first introduce and formalize a novel attacker model named Scan and Foothold Attack(SFA)based on cyber kill chain.Afterwards,we develop probabilistic models for SFA defenses to provide a deeper analysis of the theoretical effect under different defense strategies.These models quantify attack success probability and the probability that the attacker will be deceived under various conditions,such as the size of address space,and the number of hosts,attack analysis time.Finally,the experimental results show that the actual defense effect of each strategy almost perfectly follows its probabilistic model.Also,the defense strategy of combining address mutation and fingerprint camouflage can achieve a better defense effect than the single address mutation.

Assessing target optical camouflage effects using brain functional networks:A feasibility study

Brain functional networks model the brain's ability to exchange information across different regions,aiding in the understanding of the cognitive process of human visual attention during target searching,thereby contributing to the advancement of camouflage evaluation.In this study,images with various camouflage effects were presented to observers to generate electroencephalography(EEG)signals,which were then used to construct a brain functional network.The topological parameters of the network were subsequently extracted and input into a machine learning model for training.The results indicate that most of the classifiers achieved accuracy rates exceeding 70%.Specifically,the Logistic algorithm achieved an accuracy of 81.67%.Therefore,it is possible to predict target camouflage effectiveness with high accuracy without the need to calculate discovery probability.The proposed method fully considers the aspects of human visual and cognitive processes,overcomes the subjectivity of human interpretation,and achieves stable and reliable accuracy.

Towards complex scenes: A deep learning-based camouflaged people detection method for snapshot multispectral images

Camouflaged people are extremely expert in actively concealing themselves by effectively utilizing cover and the surrounding environment. Despite advancements in optical detection capabilities through imaging systems, including spectral, polarization, and infrared technologies, there is still a lack of effective real-time method for accurately detecting small-size and high-efficient camouflaged people in complex real-world scenes. Here, this study proposes a snapshot multispectral image-based camouflaged detection model, multispectral YOLO(MS-YOLO), which utilizes the SPD-Conv and Sim AM modules to effectively represent targets and suppress background interference by exploiting the spatial-spectral target information. Besides, the study constructs the first real-shot multispectral camouflaged people dataset(MSCPD), which encompasses diverse scenes, target scales, and attitudes. To minimize information redundancy, MS-YOLO selects an optimal subset of 12 bands with strong feature representation and minimal inter-band correlation as input. Through experiments on the MSCPD, MS-YOLO achieves a mean Average Precision of 94.31% and real-time detection at 65 frames per second, which confirms the effectiveness and efficiency of our method in detecting camouflaged people in various typical desert and forest scenes. Our approach offers valuable support to improve the perception capabilities of unmanned aerial vehicles in detecting enemy forces and rescuing personnel in battlefield.

What If I Told You Camouflage is a Myth? Animal Coloration is Mainly A-biotic and not Biotic (Camouflage)

In the present article,the author posits that the perception that animals apparently display a strategy of avoiding detection by means of camouflage—i.e.,by disguising themselves in the natural colours of their environment—is not the actual case in nature but,rather,merely anecdotal.Animal coloration is mainly a-biotic(eco-physiological)and not biotic(camouflage).The contention regarding the absence of the phenomenon of camouflage among animals as a common evolutionary response is based on three arguments:(1)that reflecting the natural colours of the environment is linked to ecophysiology;(2)that predator and prey constitute“an evolutionary pair”and,accordingly,they know how to identify one another(in order to survive they employ different strategies,of which camouflage is not one of them);and (3)that the approach of relating animal camouflage to reflecting the colours of the environment is an anthropocentric one.Rather than the accepted biotic-ethological approach(colour camouflage),the present article suggests the recognition of a-biotic and eco-physiological conditions as a distinct research field,whose title“Reflection of environmental colours by animals”,along with this article,calls for eco-physiologists to demonstrate that this approach indeed offers a special contribution to the understanding of colouration in animals.

Dual Attribute Adversarial Camouflage toward camouflaged object detection

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.

A camouflage target detection method based on local minimum difference constraints

To address the problems of missing inside and incomplete edge contours in camouflaged target detection results,we propose a camouflaged moving target detection algorithm based on local minimum difference constraints(LMDC).The algorithm first uses the mean to optimize the initial background model,removes the stable background region by global comparison,and extracts the edge point set in the potential target region so that each boundary point(seed)grows along the center of the target.Finally,we define the minor difference constraints term,combine the seed path and the target space consistency,and calculate the attributes of each pixel in the potential target area to realize camouflaged moving target detection.The algorithm of this paper is verified based on a public data sofa video and test videos and compared with the five classic algorithms.The experimental results show that the proposed algorithm yields good results based on integrity,accuracy,and a number of objective evaluation indexes,and its overall performance is better than that of the compared algorithms.

A small-spot deformation camouflage design algorithm based on background texture matching

In order to solve the problem of poor fusion between the spots of deformation camouflage and the background,a small-spot deformation camouflage design algorithm based on background texture matching is proposed in this research.The combination of spots and textures improved the fusion of the spot pattern and the background.An adversarial autoencoder convolutional network was designed to extract background texture features.The image adversarial loss was added and the reconstruction loss was improved to improve the clarity of the generated texture pattern and the generalization ability of the model.The digital camouflage was formed by obtaining the mean value of the square area and replacing the main color.At the same time,the spots in the square area with a side length of 2 s were subjected to simple linear iterative clustering to form irregular small-spot camouflage.A dataset with a scale of 1050 was established in the experiment.The training results of three different loss functions were investigated.The results showed that the proposed loss function could enhance the generalization of the model and improve the quality of the generated texture image.A variety of digital camouflages with main colors and irregular small-spot camouflage were generated,and their efficiency was tested.On the one hand,intuitive evaluation was given by personnel observing the camouflage pattern embedded in the background and its contour map calculated by the canny operator.On the other hand,objective comparison result was formed by calculating the 4 evaluation indexes between the camouflage pattern and the background.Both results showed that the generated pattern had a high degree of fusion with the background.This model could balance the relationship between the spot size,the number of main colors and the actual effect according to actual needs.

基于偏振编码图像的低空伪装目标实时检测

偏振可以提高无人机的自主侦察能力,但易受到探测角度和目标材质的影响,从而降低偏振检测的鲁棒性。为此,提出一种基于偏振图像的低空伪装目标实时检测算法YOLO-P,采用融合多偏振方向信息的编码图像作为输入,应用三维卷积模块提取不同偏振方向图像之间的联系特征;引入特征增强模块对多层次特征进行进一步增强;采用跨层级特征聚合网络,充分利用不同尺度的特征信息,完成特征的有效聚合,最终联合多通道特征信息输出检测结果。构建包含10类目标的低空伪装目标偏振图像数据集PICO(Polarization Image of Camouflaged Objects)。在PICO数据集上的实验结果表明,新方法可以有效检测伪装目标,mAP_(0.5:0.95)达到52.0%,mAP_(0.5)达到91.5%,检测速率达到55.0帧/s,满足实时性要求。

语义重建的动态监督伪装物体检测

伪装物体检测旨在分离视觉上高度融入周围环境的物体,但是物体前景与背景存在大量相似干扰,导致分割过程中易于出现明显错误.针对上述问题,文中提出基于语义重建的动态监督伪装物体检测网络(Dynamic Supervised Camouflaged Object Detection Network with Semantic Reconstruction,DSSRNet),通过重建特征图的空间语义和引入置信度指导网络训练,实现对伪装物体的准确分割.首先,提出空间语义低秩重建机制,精细感知不同尺度下伪装物体具有区分性的语义特征.然后,生成预测置信度图,对分割过程进行动态监督,减少网络因过于自信造成的假阳性和假阴性判断.最后,提出模糊感知损失函数,对网络施加强约束,改善预测时产生的图像模糊问题.在3个具有挑战性的基准数据集上的实验表明,DSSRNet可较好地排除相似信息干扰,取得精准的分割效果.

基于特征波段偏振成像的差异增强伪装目标检测

光谱偏振探测技术利用目标多维度信息,提高伪装目标检测的精度和可靠性。现有的光谱偏振成像系统产生的高维度数据难以实时解算,复杂场景下光谱偏振探测的性能不佳。为此,提出一种基于特征波段偏振成像系统的伪装目标检测算法。针对目标场景筛选特征波段,定制751 nm窄带滤光片结合快照式偏振阵列相机,构建特征波段偏振图像采集系统,实时获取目标图像。提出差异增强和交织序列融合检测算法,设计偏振参数图像,增强特征波段偏振图像的目标对比度;融合差异增强和交织序列映射结果,对目标图像背景噪声进行抑制,进一步突出目标特征;通过阈值分割提取伪装目标。实验结果表明:所提的伪装目标检测算法在不同场景下的综合评价指标F均在0.90以上,检测速度达到20帧/s,实现了复杂场景下伪装目标的快速精准探测。

基于Transformer的跨尺度交互学习伪装目标检测

伪装目标检测(COD)旨在精确且高效地检测出与背景高度相似的伪装物体,其方法可为物种保护、医学病患检测和军事监测等领域提供助力,具有较高的实用价值.近年来,采用深度学习方法进行伪装目标检测成为一个比较新兴的研究方向.但现有大多数COD算法都是以卷积神经网络(CNN)作为特征提取网络,并且在结合多层次特征时,忽略了特征表示和融合方法对检测性能的影响.针对基于卷积神经网络的伪装目标检测模型对被检测目标的全局特征提取能力较弱问题,提出一种基于Transformer的跨尺度交互学习伪装目标检测方法.该模型首先提出了双分支特征融合模块,将经过迭代注意力的特征进行融合,更好地融合高低层特征;其次引入了多尺度全局上下文信息模块,充分联系上下文信息增强特征;最后提出了多通道池化模块,能够聚焦被检测物体的局部信息,提高伪装目标检测准确率.在CHAMELEON、CAMO以及COD10K数据集上的实验结果表明,与当前主流的伪装物体检测算法相比较,该方法生成的预测图更加清晰,伪装目标检测模型能取得更高精度.

基于特征聚合与边缘检测的伪装目标检测

针对伪装目标边缘模糊、相关检测模型上下文特征利用率低、边缘特征融合繁琐的问题,提出一种基于特征融合与边缘检测的伪装目标检测模型F2-EDNet。首先构造特征增强模块,细化主干网络的多尺度上下文特征,有效增强伪装目标特征信息;同时,引入跨层特征引导的边缘预测支路以集成来自主干网络底层和顶层的跨层特征,在辅助检测伪装目标边缘的同时,提取边缘特征;最后,提出多尺度特征聚合模块,通过结合注意力机制,充分融合边缘特征与上下文特征,有效提高预测精度。实验结果表明,F2-EDNet在公开数据集CAMO、COD10K和NC4K上的结构相似性、平均精度与召回率、相关性、平均误差指标均值分别提高了1.41%、1.74%、0.14%、0.77%;和同类模型相比,该模型具有更丰富的边缘,定位伪装区域更准确;在实际应用中,模型检测速率可达46帧/s,证明模型具有较好的实时检测能力。

数码迷彩图案设计成型及伪装效果评价研究进展

数码迷彩是由数码成像和图像处理技术相结合生成的多色块状单元所构成的迷彩图案,在军事伪装领域运用广泛。为促进数码迷彩设计与评价领域完整体系的建立,从数码迷彩图案设计、迷彩成型和伪装效果评价方面,介绍数码迷彩的发展过程和发展现状,总结目前研究中存在的问题,展望未来发展趋势。研究认为,当前数码迷彩研究聚焦于计算机模拟与虚拟环境评价,实地应用评价欠缺。未来可运用喷墨印花技术及多功能墨水,结合实地环境评价,扩大数码迷彩在纺织服装领域的应用。

一种伪装昆虫图像的前背景自动分割算法——ZDNet

【目的】昆虫常在色彩、纹理或形态上和背景相似,具有伪装性,识别难度大。本研究旨在探索基于深度学习的伪装昆虫前背景自动分割方法。【方法】将显著目标检测算法(salient object detection algorithm)、大模型图像分割算法(large-scale model-based image segmentation algorithm)以及伪装目标检测算法(camouflaged object detection algorithm)应用于伪装昆虫数据集,该数据集包括10类昆虫共1 900张图片;并进一步针对现有伪装目标检测算法的不足,提出了一种基于DGNet(deep-gradient network)的网络模型改进方法,即ZDNet(zoom-deep gradient network)。在构建该模型时,充分运用图像特征增强、交错图像金字塔、梯度诱导和跳跃式特征融合等技术。利用伪装目标检测公开数据集COD10K与CAMO构建了包含螽斯、蜘蛛等10个目昆虫的图像数据集,结合迁移学习进行网络训练,将经过训练的模型用于分割伪装昆虫。【结果】现有的伪装目标检测模型用于伪装昆虫前背景分割时,其分割性能明显优于显著目标检测模型和大模型分割图像。同时,ZDNet在性能上也明显优于现有的伪装目标检测算法,获得的S度量值、最大F度量值、平均F度量值、最大E度量值、平均E度量值和平均绝对误差(mean absolute error,MAE)分别为0.890, 0.865, 0.824, 0.966, 0.951和0.020。【结论】研究结果证明了ZDNet网络模型能够获得很好的伪装昆虫前背景分割结果,有利于提高昆虫识别的性能,也进一步拓宽了伪装目标检测方法的应用范围。

基于密集多尺度自注意力变换网络的伪装对象分割方法

为了充分地发挥深度自注意力变换网络在伪装对象分割任务中的潜力,提出一种基于密集多尺度自注意力变换网络的伪装对象分割方法,包含双分支可分离密集多尺度特征提取和快速注意力诱导的跨级交互融合2个模块.首先以自注意力变换网络作为骨干特征提取器获取各级特征;然后将提取的特征馈送到双分支可分离密集多尺度特征提取模块,在局部分支和全局分支中,利用密集递进相连的深度可分离卷积块提取丰富的多尺度上下文特征;最后使用快速注意力诱导的跨级交互融合模块融合各级特征,并利用每级融合特征预测伪装映射,通过深度监督让各级特征在空间上保持高度一致性,尽可能地集中注意力于伪装特征而避免背景噪声的干扰.在CHAMELEON,CAMO,COD10K和NC4K这4个基准数据集上,与其他28种主流模型进行定性可视化对比,以及针对PR曲线、S值、F值、E值及MAE这5种评价指标的定量对比实验结果表明,所提出的基于密集多尺度自注意力变换网络是一种有效的伪装对象分割模型.

基于边缘填充的单兵迷彩伪装小目标检测

针对迷彩单兵识别存在伪装对象与背景高度相似融合、目标尺寸小等问题,提出了基于边缘填充的单兵迷彩伪装小目标检测模型BFNet(boundary-filled network)。该网络以SCNet(sparse complex-valued neural network)作为骨干网络,在网络的边缘引导阶段,利用边缘先验信息以及边缘的周围环境来挖掘目标信息。在上下文聚合阶段,利用上一级的预测值,使网络学习预测背景与前景的相互关系。实验结果表明:与最先进的BGNet相比,BFNet平均精度提升了0.74%,交并比识别率提升了1.35%,同时自适应E度量、加权F度量以及结构相似度与加权自适应F度量均得到了提高,其中,自适应E度量提升了0.85%,加权F度量提升了0.71%,证明所提出的BFNet能在更大程度上识别出单兵迷彩伪装小目标,且识别精度也得到提升。

基于背景拼接与纹理模板的全自动迷彩图案设计

为提高迷彩伪装图案的设计效率和环境适应性,利用背景拼接与纹理模板融合进行迷彩伪装图案的全自动化设计。通过将输入的若干背景图像自动组合为单张拼接图像,采用均值聚类法提取出背景拼接图像的主色;提出4种迷彩纹理模板自动设计方法(多圆形随机分布法、WGN傅里叶频谱法、肌理图像生成法及分层云彩法),再对所得迷彩纹理模板进行色彩聚类与色彩替换,得到最终的迷彩伪装图案。为验证设计的有效性,针对丛林虚拟场景自动生成了4种伪装图案,并进行了伪装效果的主观和客观评价。结果表明,上述方法仅需17.0~71.0 s即可完成针对目标背景的迷彩图案设计,且最优方案为基于多圆形随机分布法纹理模板的迷彩图案;本文方法相对于普通迷彩伪装,其主观评价搜索时间增加了8.1%,基于Positioning and Focus Network(PF-Net)模型的客观评价发现概率降低了45%。

基于边缘增强和特征融合的伪装目标分割

伪装目标分割的任务是使用像素级分割掩码将与背景高度相似的目标进行准确分类和定位,与传统的目标分割任务相比更具挑战性。针对目标与周围环境高度相似、边界模糊、对比度低等问题,构建了一种基于边缘增强和特征融合的伪装目标分割方法。首先,设计了一组边缘提取模块,能够更准确地分割有效的边缘先验。之后,引入了多尺度特征增强模块和跨层级特征聚合模块,分别挖掘层内与层间的多尺度上下文信息。提出了一种简单的层间注意力模块,利用相邻层级间的差异有效滤除融合后存在的干扰信息。最后,通过将各级特征图与边缘先验逐级结合的方式,获得准确的预测结果。实验结果表明,在4个伪装目标基准数据集上,该模型的表现都优于其他算法。其中加权F值提升了2.4%,平均绝对误差减少了7.2%,在RTX 2080Ti硬件环境下分割速度达到了44.2 FPS。与现有方法相比,该算法能够更准确地分割伪装目标。

Multifunctional MXene/Carbon Nanotube Janus Film for Electromagnetic Shielding and Infrared Shielding/Detection in Harsh Environments

Multifunctional,flexible,and robust thin films capable of operating in demanding harsh temperature environments are crucial for various cutting-edge applications.This study presents a multifunctional Janus film integrating highly-crystalline Ti_(3)C_(2)T_(x) MXene and mechanically-robust carbon nanotube(CNT)film through strong hydrogen bonding.The hybrid film not only exhibits high electrical conductivity(4250 S cm^(-1)),but also demonstrates robust mechanical strength and durability in both extremely low and high temperature environments,showing exceptional resistance to thermal shock.This hybrid Janus film of 15μm thickness reveals remarkable multifunctionality,including efficient electromagnetic shielding effectiveness of 72 dB in X band frequency range,excellent infrared(IR)shielding capability with an average emissivity of 0.09(a minimal value of 0.02),superior thermal camouflage performance over a wide temperature range(−1 to 300℃)achieving a notable reduction in the radiated temperature by 243℃ against a background temperature of 300℃,and outstanding IR detection capability characterized by a 44%increase in resistance when exposed to 250 W IR radiation.This multifunctional MXene/CNT Janus film offers a feasible solution for electromagnetic shielding and IR shielding/detection under challenging conditions.

融合上下文感知和背景探索的伪装目标检测方法

伪装目标检测(camouflaged object detection, COD)旨在检测出与周围环境高度相似的伪装目标。针对目前COD方法中检测结果不完整、边缘细节模糊的问题,提出了一种融合上下文感知和背景探索(CABENet)的伪装目标检测模型。首先,该模型利用Swin-Transformer模型作为骨干网络,在多个尺度上提取全局上下文信息;其次,利用提出的注意力联级上下文感知模块扩大感受野,并从通道和空间两个维度增强网络的特征提取能力,再通过全连接解码器捕获隐藏对象的粗略位置图;最后,通过融合注意力机制的背景探索模块从背景信息中挖掘目标的边缘线索,加强伪装目标边缘特征的提取。在CHAMELEON、CAMO以及COD10K数据集上的实验结果表明,该方法在4个评估指标上的性能优于其他10个具有代表性的模型,在COD10K数据集上,平均绝对误差降至了0.026。