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.

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 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.

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.

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.

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.

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.

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.

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.

Orthodontic-surgical treatment for severe skeletal class Ⅱ malocclusion with vertical maxillary excess and four premolars extraction: A case report

BACKGROUND Patient satisfaction with facial appearance at the end of orthodontic camouflage treatment is very important, especially for skeletal malocclusion. This case report highlights the importance of the treatment plan for a patient initially treated with four-premolar-extraction camouflage, despite indications for orthognathic surgery.CASE SUMMARY A 23-year-old male sought treatment complaining about his unsatisfactory facial appearance. His maxillary first premolars and mandibular second premolars had been extracted, and a fixed appliance had been used to retract his anterior teeth for two years without improvement. He had a convex profile, a gummy smile, lip incompetence, inadequate maxillary incisor inclination, and almost a class I molar relationship. Cephalometric analysis showed severe skeletal class Ⅱ malocclusion(A point-nasion-B point = 11.5°) with a retrognathic mandible(sella-nasion-B point = 75.9°), a protruded maxilla(sella-nasion-A point = 87.4°), and vertical maxillary excess(upper incisor to palatal plane = 33.2 mm). The excessive lingual inclination of the maxillary incisors(upper incisor to nasion-A point line =-5.5°)was due to previous treatment attempts to compensate for the skeletal class Ⅱ malocclusion. The patient was successfully retreated with decompensating orthodontic treatment combined with orthognathic surgery. The maxillary incisors were repositioned and proclined in the alveolar bone, the overjet was increased, and a space was created for orthognathic surgery, including maxillary impaction, anterior maxillary back-setting, and bilateral sagittal split ramus osteotomy to correct his skeletal anteroposterior discrepancy. Gingival display was reduced, and lip competence was restored. In addition, the results remained stable after 2 years. The patient was satisfied with his new profile as well as with the functional malocclusion at the end of treatment.CONCLUSION This case report provides orthodontists a good example of how to treat an adult with severe skeletal class Ⅱ malocclusion with vertical maxillary excess after an unsatisfactory orthodontic camouflage treatment. Orthodontic and orthognathic treatment can significantly correct a patient’s facial appearance.

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.