Spear and Shield： Evolution of Integrated Circuit Camouflaging

Intellectual property （IP） protection is one of the hardcore problems in hardware security. Semiconductor industry still lacks effective and proactive defense to shield IPs from reverse engineering （RE） based attacks. Integrated circuit （IC） camouflaging technique fills this gap by replacing some conventional logic gates in tile IPs with specially designed logic cells （called camouflaged gates） without changing the functions of tile IPs. The camouflaged gates can perform different logic functions while maintaining an identical look to RE attackers, thus preventing them from obtaining the layout information of the IP directly from RE tools. Since it was first proposed in 2012, circuit camouflaging has become one of the hottest research topics in hardware security focusing on two fundamental problems. How to choose the types of camouflaged gates and decide where to insert them in order to simultaneously minimize the performance overhead and optimize the RE complexity？ How can an attacker de-camouflage a camouflaged circuit and complete the RE attack？ In this article, we review the evolution of circuit camouflaging through this spear and shield race. First, we introduce the design methods of four different kinds of camouflaged ceils based on true/dummy contacts, static random access memory （SRAM）, doping, and emerging devices, respectively. Then we elaborate four representative de-camouflaging attacks： brute force attack, IC testing based attack, satisfiability-based （SAT-based） attack, and the circuit partition based attack, and the corresponding countermeasures： clique-based camouflaging, CamoPerturb, AND-tree camouflaging, and equivalent class based camouflaging, respectively. We argue that the current research efforts should be on reducing overhead introduced by circuit camouflaging and defeating decamouflaging attacks. We point out that exploring features of emerging devices could be a promising direction. Finally, as a complement to circuit camouflaging, we conclude with a brief review of other state-of-the-art IP protection techniques.

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.

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.

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.

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.

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.

Microwave absorption properties of carbonyl iron-based paint coatings for military applications

The paper explores the possibilities of using carbonyl iron in the form of a powder for the manufacture of radar-absorbing paints-reducing the radar signature of the objects that they cover.The attenuation values in the range of 4-18 GHz for various coating thicknesses,ranging from 0.5 to 2.00 mm with 0.5 mm increment,and for different absorber content-75%and 80%,as well as the use of two different binders in the form of epoxy resins with hardeners,were investigated.For the frequency of 18 GHz and a 1.5 mm thick coating with a 75%absorber content,Epidian 112 resin and Saduramid 10/50 hardener used as a binder,and the maximum attenuation level obtained equalled 20.2 d B at 16 GHz.Additionally,the absorber particle size ranging from 3 to 4μm and its higher mass content resulted in achieving the reflection loss above-12 d B in the entire 8-12.5 GHz range for layers between 1-and 1.5 mm thickness.The qualitative assessment of the tested samples in the context of camouflage in the radar range was also performed,using statistical analysis.

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.

Research on extraction and reproduction of deformation camouflage spot based on generative adversarial network model

The method of describing deformation camouflage spots based on feature space has some shortcomings,such as inaccurate description and difficult reproduction.Depending on the strong fitting ability of the generative adversarial network model,the distribution of deformation camouflage spot pattern can be directly fitted,thus simplifying the process of spot extraction and reproduction.The requirements of background spot extraction are analyzed theoretically.The calculation formula of limiting the range of image spot pixels is given and two kinds of spot data sets,forestland and snowfield,are established.Spot feature is decomposed into shape,size and color features,and a GAN(Generative Adversarial Network)framework is established.The effects of different loss functions on network training results are analyzed in the experiment.In the meantime,when the input dimension of generator network is 128,the balance between sample diversity and quality can be achieved.The effects of sample generation are investigated in two aspects.Subjectively,the probability of the generated spots being distinguished in the background is counted,and the results are all less than 20% and mostly close to zero.Objectively,the features of the spot shape are calculated and the independent sample T-test is applied to verify that the features are from the same distribution,and all the P-Values are much higher than 0.05.Both subjective and objective methods prove that the spots generated by this method are similar to the background spots.The proposed method can directly generate the desired camouflage pattern spots,which provides a new technical method for the deformation camouflage pattern design and camouflage effect evaluation.

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.

Study on the Camouflage-Protective and Dyeing Properties of Natural Dye Indigo

There are two major camouflage protections in modern military tactics:UV-protection and near infrared camouflage.However,not all natural and composite dyestuffs provide the mentioned properties.In this study,the cotton fabric was dyed with natural indigo and the natural indigo dyeing process was optimized.Green leaves were chosen as the simulating object,and the camouflage properties of the dyed cotton fabric were evaluated.It was observed that the dyed cotton fabric had good UV-protection and near-infrared camouflage properties.The UV-protection effect was strongly dependent on the absorption characteristics of natural indigo for UV radiation.The near infrared camouflage effect was mainly dependent on the reflection spectrum characteristics of natural indigo in the near infrared waveband.

Three-dimensional cooperative guidance law for multiple missiles with impact angle constraint

This paper proposes a cooperative guidance law for attacking a ground target with the impact angle constraint based on the motion camouflage strategy in the line-of-sight(LOS)frame.A dynamic model with the impact angle constraint is established according to the relative motion between multiple missiles and the target.The process of cooperative guidance law design is divided into two stages.Firstly,based on the undirected graph theory,a new finite-time consensus protocol on the LOS direction is derived to guarantee relative distances reach consensus.And the value of acceleration command is positive,which is beneficial for engineering realization.Secondly,the acceleration command on the normal direction of the LOS is designed based on motion camouflage and finite-time convergence,which can ensure the missiles reach the target with the desired angle and satisfy the motion camouflage state.The finitetime stability analysis is proved by the Lyapunov theory.Numerical simulations for stationary and maneuver targets have demonstrated the effectiveness of the cooperative guidance law proposed.

MF-CFI:A fused evaluation index for camouflage patterns based on human visual perception

The evaluation index of camouflage patterns is important in the field of military application.It is the goal that researchers have always pursued to make the computable evaluation indicators more in line with the human visual mechanism.In order to make the evaluation method more computationally intelligent,a Multi-Feature Camouflage Fused Index(MF-CFI)is proposed based on the comparison of grayscale,color and texture features between the target and the background.In order to verify the effectiveness of the proposed index,eye movement experiments are conducted to compare the proposed index with existing indexes including Universal Image Quality Index(UIQI),Camouflage Similarity Index(CSI)and Structural Similarity(SSIM).Twenty-four different simulated targets are designed in a grassland background,28 observers participate in the experiment and record the eye movement data during the observation process.The results show that the highest Pearson correlation coefficient is observed between MF-CFI and the eye movement data,both in the designed digital camouflage patterns and largespot camouflage patterns.Since MF-CFI is more in line with the detection law of camouflage targets in human visual perception,the proposed index can be used for the comparison and parameter optimization of camouflage design algorithms.

Background dominant colors extraction method based on color image quick fuzzy c-means clustering algorithm

A quick and accurate extraction of dominant colors of background images is the basis of adaptive camouflage design.This paper proposes a Color Image Quick Fuzzy C-Means(CIQFCM)clustering algorithm based on clustering spatial mapping.First,the clustering sample space was mapped from the image pixels to the quantized color space,and several methods were adopted to compress the amount of clustering samples.Then,an improved pedigree clustering algorithm was applied to obtain the initial class centers.Finally,CIQFCM clustering algorithm was used for quick extraction of dominant colors of background image.After theoretical analysis of the effect and efficiency of the CIQFCM algorithm,several experiments were carried out to discuss the selection of proper quantization intervals and to verify the effect and efficiency of the CIQFCM algorithm.The results indicated that the value of quantization intervals should be set to 4,and the proposed algorithm could improve the clustering efficiency while maintaining the clustering effect.In addition,as the image size increased from 128×128 to 1024×1024,the efficiency improvement of CIQFCM algorithm was increased from 6.44 times to 36.42 times,which demonstrated the significant advantage of CIQFCM algorithm in dominant colors extraction of large-size images.

Design of infrared camouflage cloak for underground silos

The temperature difference between the exposed surface of an underground silo and the surrounding soil surface is significant,which means a silo can be easily found by infrared detection.We designed an infrared camouflage cloak consisting of an imitative layer and an insulation layer for the silos.The imitative layer is used to imitate the thermal response of the soil to the surrounding environment.The insulation layer is used to weaken the impact of the internal temperature field of the silo on the lower boundary of the imitative layer.A silo model including surrounding soil and a soil model without silo were established,and the influences of the material and thickness of each layer on the infrared camouflage effect were analyzed.The results show that when using a silicone rubber containing alumina powder with a volume fraction of 3.18% as the imitative material,its thermal inertia is in consistent with that of the soil.Meanwhile,it was found that the thickness of the imitative layer doesn't need to be greater than its thermal penetration depth to achieve the infrared camouflage,and the absence of the insulation layer will cause hot spots on the silo surface in winter to weaken the camouflage effect.The optimized thicknesses of the imitative layer and the insulation layer are 22 cm and 4 cm re spectively.The simulations indicate that with the application of the cloak,the maximum value of the absolute values of the temperature differences between the average temperatures of the silo surface and the surrounding soil surface temperatures drops from 1,59℃ to 0.31℃ in summer and from 1.92℃ to 0.21℃ in winter.This designed cloak can achieve an all-weather and full-time passive infrared camouflage.