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.

Camouflaged Uptake Following Incidental Focus-on-Form Episodes

The efficacy of focus-on-form(FonF)within the context of communicatively-oriented language activities is measured via uptake.Uptake is defined as learners’verbal responses immediately following either preemptive or reactive FonF instruction(Loewen,2004).The present study investigated what is(not)meant and(not)measured through this definition of uptake.Drawing on the audio-recorded analysis of 20 hours of communicatively–oriented interactions in an intermediate IELTS class with two teachers,this study investigates the frequency of preemptive and reactive incidental FonF,and the subsequent occurrence of uptake in an English as a foreign language context.This study also provided an in-depth qualitative analysis of these classes through field notes,learner notes,and video-recorded data to explore the instances of uptake moves that were not captured through audio-recorded data.The quantitative findings of this study demonstrated a very low and disappointing uptake rate.Furthermore,the study did not find a significant difference between reactive and preemptive FonF in terms of uptake rate.Nonetheless,the qualitative data revealed a myriad of uptake instances not observable via the initial data analysis.Based on these findings,a new definition of uptake is suggested,which includes camouflaged uptake and learners’immediate oral responses to FonF.Since uptake is used to gauge the efficacy of incidental FonF in primarily meaning–oriented classes,it is concluded that audio-recorded data just show the tip of the iceberg as far as the uptake rate is concerned.Thus,second language acquisition researchers are recommended to employ multiple indices to examine the effectiveness of FonF instruction.

RBC Membrane Camouflaged Semiconducting Polymer Nanoparticles for Near-Infrared Photoacoustic Imaging and Photothermal Therapy

Semiconducting conjugated polymer nanoparticles(SPNs)represent an emerging class of phototheranostic materi-als with great promise for cancer treatment.In this report,low-bandgap electron donoracceptor(DA)-conjugated SPNs with sur-face cloaked by red blood cell membrane(RBCM)are developed for highly e ective photoacoustic imaging and photothermal therapy.The resulting RBCM-coated SPN(SPN@RBCM)displays remarkable near-infrared light absorption and good photosta-bility,as well as high photothermal conver-sion e ciency for photoacoustic imaging and photothermal therapy.Particularly,due to the small size(<5 nm),SPN@RBCM has the advantages of deep tumor penetration and rapid clearance from the body with no appreciable toxicity.The RBCM endows the SPNs with prolonged systematic circulation time,less reticuloendothelial system uptake and reduced immune-recognition,hence improving tumor accumulation after intravenous injection,which provides strong photoacoustic signals and exerts excellent photothermal therapeutic e ects.Thus,this work provides a valuable paradigm for safe and highly e cient tumor pho-toacoustic imaging and photothermal therapy for further clinical translation.

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.

Camouflaged target detection based on multimodal image input pixel-level fusion

Camouflaged targets are a type of nonsalient target with high foreground and background fusion and minimal target feature information,making target recognition extremely difficult.Most detection algorithms for camouflaged targets use only the target’s single-band information,resulting in low detection accuracy and a high missed detection rate.We present a multimodal image fusion camouflaged target detection technique (MIF-YOLOv5) in this paper.First,we provide a multimodal image input to achieve pixel-level fusion of the camouflaged target’s optical and infrared images to improve the effective feature information of the camouflaged target.Second,a loss function is created,and the K-Means++clustering technique is used to optimize the target anchor frame in the dataset to increase camouflage personnel detection accuracy and robustness.Finally,a comprehensive detection index of camouflaged targets is proposed to compare the overall effectiveness of various approaches.More crucially,we create a multispectral camouflage target dataset to test the suggested technique.Experimental results show that the proposed method has the best comprehensive detection performance,with a detection accuracy of 96.5%,a recognition probability of92.5%,a parameter number increase of 1×10^(4),a theoretical calculation amount increase of 0.03 GFLOPs,and a comprehensive detection index of 0.85.The advantage of this method in terms of detection accuracy is also apparent in performance comparisons with other target algorithms.

Ultrafast laser processing of camouflaged metals by topography inherited multistep removal for information encryption

Surfaces with micro-nanoscale structures show different optical responses,including infrared reflection,thermal radiation,and protective coloration.Direct realization of structure camouflage is important for material functionalities.However,external cloaks or coatings are necessary in structure camouflage,which limits the surface functionality.Here,we propose a novel strategy for the direct structure camouflage through topography inherited removal(TIR)with ultrafast laser,featuring pristine topography preservation and scattering surface fabrication.After multistep TIR,pristine topographies are partially and uniformly removed to preserve the original designed structures.Optical response changes show the suppression of specular reflection by uniformizing reflected light intensity to a low level on the inherited surface.We produce various structure camouflages on large scaled substrates,and demonstrate applications of information encryption in code extraction and word recognition through structure camouflage.The proposed strategy opens opportunities for infrared camouflage and other technologies,such as thermal management,device security,and information encryption.

Cancer cell membrane camouflaged biomimetic gelatin-based nanogel for tumor inhibition

Enhancing the active tumor targeting ability and decreasing the clearance of reticuloendothelial system(RES)are important issues for drug delivery systems(DDSs)in cancer therapy.In recent years,cell membrane camouflage,as one of the biomimetic modification strategies,has shown huge potential.Many natural properties of source cells can be inherited,allowing the DDSs to successfully avoid phagocytosis by macrophages,prolong circulation time,and achieve homologous targeting to lesion tissue.In this study,a cancer cell membrane camouflaged nanoplatform based on gelatin with a typical core-shell structure was developed for cancer chemotherapy.Doxorubicin(DOX)loaded gelatin nanogel(NG@DOX)acted as the inner core,and 4T1(mouse breast carcinoma cell)membrane was set as the outer shell(M-NG@DOX).The M-NG platform enhanced the ability of homologous targeting due to the surface protein of cell membrane being completely retained,which could promote the cell uptake of homotypic cells,avoid phagocytosis by RAW264.7 macrophages,and therefore increase accumulation in tumor tissue.Meanwhile,due to the better controlled drug release capability of M-NG@DOX,premature release of DOX in circulation could be reduced,minimizing side effects in common chemotherapy.As a result,the biomimetic nanoplatform in this study,obtained by a cancer cell membrane camouflaged drug delivery system,efficiently reached desirable tumor elimination,providing a significant strategy for effective targeted therapy and specific carcinoma therapy.

Deep Gradient Learning for Efficient Camouflaged Object Detection

This paper introduces deep gradient network(DGNet),a novel deep framework that exploits object gradient supervision for camouflaged object detection(COD).It decouples the task into two connected branches,i.e.,a context and a texture encoder.The es-sential connection is the gradient-induced transition,representing a soft grouping between context and texture features.Benefiting from the simple but efficient framework,DGNet outperforms existing state-of-the-art COD models by a large margin.Notably,our efficient version,DGNet-S,runs in real-time(80 fps)and achieves comparable results to the cutting-edge model JCSOD-CVPR21 with only 6.82%parameters.The application results also show that the proposed DGNet performs well in the polyp segmentation,defect detec-tion,and transparent object segmentation tasks.The code will be made available at https://github.com/GewelsJI/DGNet.

Precise assembly of inside-out cell membrane camouflaged nanoparticles via bioorthogonal reactions for improving drug leads capturing

Cell membrane camouflaged nanoparticles have been widely used in the field of drug leads discovery attribute to their unique biointerface targeting function.However,random orientation of cell membrane coating does not guarantee effective and appropriate binding of drugs to specific sites,especially when applied to intracellular regions of transmembrane proteins.Bioorthogonal reactions have been rapidly developed as a specific and reliable method for cell membrane functionalization without disturbing living biosystem.Herein,inside-out cell membrane camouflaged magnetic nanoparticles(IOCMMNPs)were accurately constructed via bioorthogonal reactions to screen small molecule inhibitors targeting intracellular tyrosine kinase domain of vascular endothelial growth factor recptor-2.Azide functionalized cell membrane acted as a platform for specific covalently coupling with alkynyl functionalized magnetic Fe_(3)O_(4)nanoparticles to prepare IOCMMNPs.The inside-out orientation of cell membrane was successfully verified by immunogold staining and sialic acid quantification assay.Ultimately,two compounds,senkyunolide A and ligustilidel,were successfully captured,and their potential antiproliferative activities were further testified by pharmacological experiments.It is anticipated that the proposed inside-out cell membrane coating strategy endows tremendous versatility for engineering cell membrane camouflaged nanoparticles and promotes the development of drug leads discovery platforms.

Immune-regulating camouflaged nanoplatforms:A promising strategy to improve cancer nano-immunotherapy

Although nano-immunotherapy has advanced dramatically in recent times,there remain two significant hurdles related to immune systems in cancer treatment,such as(namely)inevitable immune elimination of nanoplat-forms and severely immunosuppressive microenvironment with low immunogenicity,hampering the perfor-mance of nanomedicines.To address these issues,several immune-regulating camouflaged nanocomposites have emerged as prevailing strategies due to their unique characteristics and specific functionalities.In this review,we emphasize the composition,performances,and mechanisms of various immune-regulating camouflaged nano-platforms,including polymer-coated,cell membrane-camouflaged,and exosome-based nanoplatforms to evade the immune clearance of nanoplatforms or upregulate the immune function against the tumor.Further,we discuss the applications of these immune-regulating camouflaged nanoplatforms in directly boosting cancer immunotherapy and some immunogenic cell death-inducing immunotherapeutic modalities,such as chemo-therapy,photothermal therapy,and reactive oxygen species-mediated immunotherapies,highlighting the cur-rent progress and recent advancements.Finally,we conclude the article with interesting perspectives,suggesting future tendencies of these innovative camouflaged constructs towards their translation pipeline.

An Efficient Technique to Reverse Engineer Minterm Protection Based Camouflaged Circuit

Integrated circuit （IC） camouflaging technique has been applied as a countermeasure against reverse engineering （RE）. However, its effectiveness is threatened by a boolean satisfiability （SAT） based de-camouflaging attack, which is able to restore the camouflaged circuit within only minutes. As a defense to the SAT-based de-camouflaging attack, a brand new camouflaging strategy （called CamoPerturb） has been proposed recently, which perturbs one minterm by changing one gate＇s functionality and then restores the perturbed circuit with a separated camouflaged block, achieving good resistance against the SAT-based attack. In this paper, we analyze the security vulnerabilities of CamoPerturb by illustrating the mechanism of minterm perturbation induced by gate replacement, then propose an attack to restore the changed gate＇s functionality, and recover the camouflaged circuit. The attack algorithm is facilitated by sensitization and implication principles in automatic test pattern generation （ATPG） techniques. Experimental results demonstrate that our method is able to restore the camouflaged circuits with very little time consumption.

A Camouflaged Film Imitating the Chameleon Skin with Color-Changing Microfluidic Systems Based on the Color Information Identification of Background

To adapt to a complex and variable environment,self-adaptive camouflage technology is becoming more and more important in all kinds of military applications by overcoming the weakness of the static camouflage.In nature,the chameleon can achieve self-adaptive camouflage by changing its skin color in real time with the change of the background color.To imitate the chameleon skin,a camouflaged film controlled by a color-changing microfluidic system is proposed in this paper.The film with microfluidic channels fabricated by soft materials can achieve dynamic cloaking and camouflage by circulating color liquids through channels inside the film.By sensing and collecting environmental color change information,the control signal of the microfluidic system can be adjusted in real time to imitate chameleon skin.The microstructure of the film and the working principle of the microfluidic color-changing system are introduced.The mechanism to generate the control signal by information processing of background colors is illustrated.“Canny”double-threshold edge detection algorithm and color similarity are used to analyze and evaluate the camouflage.The tested results show that camouflaged images have a relatively high compatibility with environmental backgrounds and the dynamic cloaking eff ect can be achieved.

Confusing Object Detection:A Survey

Confusing object detection(COD),such as glass,mirrors,and camouflaged objects,represents a burgeoning visual detection task centered on pinpointing and distinguishing concealed targets within intricate backgrounds,leveraging deep learning methodologies.Despite garnering increasing attention in computer vision,the focus of most existing works leans toward formulating task-specific solutions rather than delving into in-depth analyses of methodological structures.As of now,there is a notable absence of a comprehensive systematic review that focuses on recently proposed deep learning-based models for these specific tasks.To fill this gap,our study presents a pioneering review that covers both themodels and the publicly available benchmark datasets,while also identifying potential directions for future research in this field.The current dataset primarily focuses on single confusing object detection at the image level,with some studies extending to video-level data.We conduct an in-depth analysis of deep learning architectures,revealing that the current state-of-the-art(SOTA)COD methods demonstrate promising performance in single object detection.We also compile and provide detailed descriptions ofwidely used datasets relevant to these detection tasks.Our endeavor extends to discussing the limitations observed in current methodologies,alongside proposed solutions aimed at enhancing detection accuracy.Additionally,we deliberate on relevant applications and outline future research trajectories,aiming to catalyze advancements in the field of glass,mirror,and camouflaged object detection.

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.

Nature's disguise:Empirical demonstration of dead-leaf masquerade in Kallima butterflies

Animals deploy diverse color-based defenses against predators,including crypsis,mimicry,aposematism,and masquerade.While crypsis,mimicry,aposematism have been extensively studied,the strategy of masquerade-where organisms imitate inedible or inanimate objects such as leaves,twigs,stones,and bird droppings-remains comparatively underexplored,particularly in adult butterflies.The Indian oakleaf butterfly(Kallima inachus)exemplifies this phenomenon,with its wings resembling dead leaves,providing a classic example of natural selection.Although it has long been postulated that these butterflies evade predation by being misidentified as dead leaves,direct experimental evidence is lacking.In the current study,using domestic chicks as predators,we manipulated their prior experience with dead leaves(model objects)while maintaining constant exposure to butterflies to test whether dead-leaf masquerade provides a protective advantage by preventing recognition.Results showed a marked delay in the initiation of attacks by chicks familiar with dead leaves compared to those with no prior exposure or those exposed to visually altered leaves.Chicks with prior dead-leaf experience required a similar amount of time to attack the butterflies as they did to attack dead leaves.These findings provide the first empirical demonstration of dead-leaf masquerade in Kallima butterflies,shedding light on its evolutionary significance.Our study highlights the effectiveness of masquerade in inducing the misclassification of butterflies as inanimate objects,showcasing the precise mimicry achieved by these organisms when viewed in isolation from the model objects.This study advances our understanding of the evolution of masquerade and its role as a potent antipredator strategy in nature.

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.

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.

Transformation of MRI Images to Three-Level Color Spaces for Brain Tumor Classification Using Deep-Net

In the domain ofmedical imaging,the accurate detection and classification of brain tumors is very important.This study introduces an advanced method for identifying camouflaged brain tumors within images.Our proposed model consists of three steps:Feature extraction,feature fusion,and then classification.The core of this model revolves around a feature extraction framework that combines color-transformed images with deep learning techniques,using the ResNet50 Convolutional Neural Network(CNN)architecture.So the focus is to extract robust feature fromMRI images,particularly emphasizingweighted average features extracted fromthe first convolutional layer renowned for their discriminative power.To enhance model robustness,we introduced a novel feature fusion technique based on the Marine Predator Algorithm(MPA),inspired by the hunting behavior of marine predators and has shown promise in optimizing complex problems.The proposed methodology can accurately classify and detect brain tumors in camouflage images by combining the power of color transformations,deep learning,and feature fusion via MPA,and achieved an accuracy of 98.72%on a more complex dataset surpassing the existing state-of-the-art methods,highlighting the effectiveness of the proposed model.The importance of this research is in its potential to advance the field ofmedical image analysis,particularly in brain tumor diagnosis,where diagnoses early,and accurate classification are critical for improved patient results.

Matching Performance among Visible and near Infrared Coating,Low Infrared Emitting Coating and Microwave Absorbing Coating

The matching performance among the visible and near infrared coating.the low infrared emitting coating and the microwave absorbing coating was investigated.Experimental results show that the resulting malerial is characteristic of wideband effect ranging from the visible,near infrared and 3-5μm,8-14μm infrared protion of the spectrum,as well as the radar region from 8 to 18GHz when these three materials form αlayerstructure material system.The microwave absorbing ability of material is hardly changed.The resonance peak moves towards lower frequency as the thickness of the visible,near infrared coating and the low infrared emitting coating increases.This problem can be resolved by controlling the thickness of the matrial.On the other hand, the infrared emissivity εof the material system increases as the thickness of the visible,near infrared coating increases.This can be resolved by increasing infrared transparency of the visible and near infrared topcoating or controlling its thickness.The experimental resulting material system has spectral reflection characteristics in visible and near infrared regions that are similar to those of the natural background.