Visible to the public Biblio

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Boato, G., Dang-Nguyen, D., Natale, F. G. B. De.  2020.  Morphological Filter Detector for Image Forensics Applications. IEEE Access. 8:13549—13560.
Mathematical morphology provides a large set of powerful non-linear image operators, widely used for feature extraction, noise removal or image enhancement. Although morphological filters might be used to remove artifacts produced by image manipulations, both on binary and gray level documents, little effort has been spent towards their forensic identification. In this paper we propose a non-trivial extension of a deterministic approach originally detecting erosion and dilation of binary images. The proposed approach operates on grayscale images and is robust to image compression and other typical attacks. When the image is attacked the method looses its deterministic nature and uses a properly trained SVM classifier, using the original detector as a feature extractor. Extensive tests demonstrate that the proposed method guarantees very high accuracy in filtering detection, providing 100% accuracy in discriminating the presence and the type of morphological filter in raw images of three different datasets. The achieved accuracy is also good after JPEG compression, equal or above 76.8% on all datasets for quality factors above 80. The proposed approach is also able to determine the adopted structuring element for moderate compression factors. Finally, it is robust against noise addition and it can distinguish morphological filter from other filters.
Kalaichelvi, T., Apuroop, P..  2020.  Image Steganography Method to Achieve Confidentiality Using CAPTCHA for Authentication. 2020 5th International Conference on Communication and Electronics Systems (ICCES). :495—499.

Steganography is a data hiding technique, which is generally used to hide the data within a file to avoid detection. It is used in the police department, detective investigation, and medical fields as well as in many more fields. Various techniques have been proposed over the years for Image Steganography and also attackers or hackers have developed many decoding tools to break these techniques to retrieve data. In this paper, CAPTCHA codes are used to ensure that the receiver is the intended receiver and not any machine. Here a randomized CAPTCHA code is created to provide additional security to communicate with the authenticated user and used Image Steganography to achieve confidentiality. For achieving secret and reliable communication, encryption and decryption mechanism is performed; hence a machine cannot decode it using any predefined algorithm. Once a secure connection has been established with the intended receiver, the original message is transmitted using the LSB algorithm, which uses the RGB color spectrum to hide the image data ensuring additional encryption.

Arunpandian, S., Dhenakaran, S. S..  2020.  DNA based Computing Encryption Scheme Blending Color and Gray Images. 2020 International Conference on Communication and Signal Processing (ICCSP). :0966–0970.

In this paper, a novel DNA based computing method is proposed for encryption of biometric color(face)and gray fingerprint images. In many applications of present scenario, gray and color images are exhibited major role for authenticating identity of an individual. The values of aforementioned images have considered as two separate matrices. The key generation process two level mathematical operations have applied on fingerprint image for generating encryption key. For enhancing security to biometric image, DNA computing has done on the above matrices generating DNA sequence. Further, DNA sequences have scrambled to add complexity to biometric image. Results of blending images, image of DNA computing has shown in experimental section. It is observed that the proposed substitution DNA computing algorithm has shown good resistant against statistical and differential attacks.

Pramanik, S., Bandyopadhyay, S. K., Ghosh, R..  2020.  Signature Image Hiding in Color Image using Steganography and Cryptography based on Digital Signature Concepts. 2020 2nd International Conference on Innovative Mechanisms for Industry Applications (ICIMIA). :665–669.
Data Transmission in network security is one of the most vital issues in today's communication world. The outcome of the suggested method is outlined over here. Enhanced security can be achieved by this method. The vigorous growth in the field of information communication has made information transmission much easier. But this type of advancement has opened up many possibilities of information being snooped. So, day-by-day maintaining of information security is becoming an inseparable part of computing and communication. In this paper, the authors have explored techniques that blend cryptography & steganography together. In steganography, information is kept hidden behind a cover image. In this paper, approaches for information hiding using both cryptography & steganography is proposed keeping in mind two considerations - size of the encrypted object and degree of security. Here, signature image information is kept hidden into cover image using private key of sender & receiver, which extracts the information from stego image using a public key. This approach can be used for message authentication, message integrity & non-repudiation purpose.
Abusukhon, A., AlZu’bi, S..  2020.  New Direction of Cryptography: A Review on Text-to-Image Encryption Algorithms Based on RGB Color Value. 2020 Seventh International Conference on Software Defined Systems (SDS). :235–239.
Data encryption techniques are important for answering the question: How secure is the Internet for sending sensitive data. Keeping data secure while they are sent through the global network is a difficult task. This is because many hackers are fishing these data in order to get some benefits. The researchers have developed various types of encryption algorithms to protect data from attackers. These algorithms are mainly classified into two categories namely symmetric and asymmetric encryption algorithms. This survey sheds light on the recent work carried out on encrypting a text into an image based on the RGB color value and held a comparison between them based on various factors evolved from the literature.
Akhtar, Z., Dasgupta, D..  2019.  A Comparative Evaluation of Local Feature Descriptors for DeepFakes Detection. 2019 IEEE International Symposium on Technologies for Homeland Security (HST). :1—5.
The global proliferation of affordable photographing devices and readily-available face image and video editing software has caused a remarkable rise in face manipulations, e.g., altering face skin color using FaceApp. Such synthetic manipulations are becoming a very perilous problem, as altered faces not only can fool human experts but also have detrimental consequences on automated face identification systems (AFIS). Thus, it is vital to formulate techniques to improve the robustness of AFIS against digital face manipulations. The most prominent countermeasure is face manipulation detection, which aims at discriminating genuine samples from manipulated ones. Over the years, analysis of microtextural features using local image descriptors has been successfully used in various applications owing to their flexibility, computational simplicity, and performances. Therefore, in this paper, we study the possibility of identifying manipulated faces via local feature descriptors. The comparative experimental investigation of ten local feature descriptors on a new and publicly available DeepfakeTIMIT database is reported.
Zhou, Z., Yang, Y., Cai, Z., Yang, Y., Lin, L..  2019.  Combined Layer GAN for Image Style Transfer*. 2019 IEEE International Conference on Computational Electromagnetics (ICCEM). :1—3.

Image style transfer is an increasingly interesting topic in computer vision where the goal is to map images from one style to another. In this paper, we propose a new framework called Combined Layer GAN as a solution of dealing with image style transfer problem. Specifically, the edge-constraint and color-constraint are proposed and explored in the GAN based image translation method to improve the performance. The motivation of the work is that color and edge are fundamental vision factors for an image, while in the traditional deep network based approach, there is a lack of fine control of these factors in the process of translation and the performance is degraded consequently. Our experiments and evaluations show that our novel method with the edge and color constrains is more stable, and significantly improves the performance compared with the traditional methods.

Lee, P., Tseng, C..  2019.  On the Layer Choice of the Image Style Transfer Using Convolutional Neural Networks. 2019 IEEE International Conference on Consumer Electronics - Taiwan (ICCE-TW). :1—2.

In this paper, the layer choices of the image style transfer method using the VGG-19 neural network are studied. The VGG-19 network is used to extract the feature maps which have their implicit meaning as a learning basis. If the layers for stylistic learning are not suitably chosen, the quality of style transferred image may not look good. After making experiments, it can be observed that the color information is concentrated on lower layers from conv1-1 to conv2-2, and texture information is concentrated on the middle layers from conv3-1 to conv4-4. As to the higher layers from conv5-1 to conv5-4, they seem to be able to depict image content well. Based on these observations, the methods of color transfer, texture transfer and style transfer are presented and make comparisons with conventional methods.

Friedrich, T., Menzel, S..  2019.  Standardization of Gram Matrix for Improved 3D Neural Style Transfer. 2019 IEEE Symposium Series on Computational Intelligence (SSCI). :1375—1382.

Neural Style Transfer based on convolutional neural networks has produced visually appealing results for image and video data in the recent years where e.g. the content of a photo and the style of a painting are merged to a novel piece of digital art. In practical engineering development, we utilize 3D objects as standard for optimizing digital shapes. Since these objects can be represented as binary 3D voxel representation, we propose to extend the Neural Style Transfer method to 3D geometries in analogy to 2D pixel representations. In a series of experiments, we first evaluate traditional Neural Style Transfer on 2D binary monochromatic images. We show that this method produces reasonable results on binary images lacking color information and even improve them by introducing a standardized Gram matrix based loss function for style. For an application of Neural Style Transfer on 3D voxel primitives, we trained several classifier networks demonstrating the importance of a meaningful convolutional network architecture. The standardization of the Gram matrix again strongly contributes to visually improved, less noisy results. We conclude that Neural Style Transfer extended by a standardization of the Gram matrix is a promising approach for generating novel 3D voxelized objects and expect future improvements with increasing graphics memory availability for finer object resolutions.

Anju, J., Shreelekshmi, R..  2019.  Modified Feature Descriptors to enhance Secure Content-based Image Retrieval in Cloud. 2019 2nd International Conference on Intelligent Computing, Instrumentation and Control Technologies (ICICICT). 1:674–680.
With the emergence of cloud, content-based image retrieval (CBIR) on encrypted domain gain enormous importance due to the ever increasing need for ensuring confidentiality, authentication, integrity and privacy of data. CBIR on outsourced encrypted images can be done by extracting features from unencrypted images and generating searchable encrypted index based on it. Visual descriptors like color descriptors, shape and texture descriptors, etc. are employed for similarity search. Since visual descriptors used to represent an image have crucial role in retrieving most similar results, an attempt to combine them has been made in this paper. The effect of combining different visual descriptors on retrieval precision in secure CBIR scheme proposed by Xia et al. is analyzed. Experimental results show that combining visual descriptors can significantly enhance retrieval precision of the secure CBIR scheme.
Choi, Seok-Hwan, Shin, Jin-Myeong, Liu, Peng, Choi, Yoon-Ho.  2019.  Robustness Analysis of CNN-based Malware Family Classification Methods Against Various Adversarial Attacks. 2019 IEEE Conference on Communications and Network Security (CNS). :1—6.

As malware family classification methods, image-based classification methods have attracted much attention. Especially, due to the fast classification speed and the high classification accuracy, Convolutional Neural Network (CNN)-based malware family classification methods have been studied. However, previous studies on CNN-based classification methods focused only on improving the classification accuracy of malware families. That is, previous studies did not consider the cases that the accuracy of CNN-based malware classification methods can be decreased under the existence of adversarial attacks. In this paper, we analyze the robustness of various CNN-based malware family classification models under adversarial attacks. While adding imperceptible non-random perturbations to the input image, we measured how the accuracy of the CNN-based malware family classification model can be affected. Also, we showed the influence of three significant visualization parameters(i.e., the size of input image, dimension of input image, and conversion color of a special character)on the accuracy variation under adversarial attacks. From the evaluation results using the Microsoft malware dataset, we showed that even the accuracy over 98% of the CNN-based malware family classification method can be decreased to less than 7%.

Kumar, Suren, Dhiman, Vikas, Koch, Parker A, Corso, Jason J..  2018.  Learning Compositional Sparse Bimodal Models. IEEE Transactions on Pattern Analysis and Machine Intelligence. 40:1032—1044.

Various perceptual domains have underlying compositional semantics that are rarely captured in current models. We suspect this is because directly learning the compositional structure has evaded these models. Yet, the compositional structure of a given domain can be grounded in a separate domain thereby simplifying its learning. To that end, we propose a new approach to modeling bimodal perceptual domains that explicitly relates distinct projections across each modality and then jointly learns a bimodal sparse representation. The resulting model enables compositionality across these distinct projections and hence can generalize to unobserved percepts spanned by this compositional basis. For example, our model can be trained on red triangles and blue squares; yet, implicitly will also have learned red squares and blue triangles. The structure of the projections and hence the compositional basis is learned automatically; no assumption is made on the ordering of the compositional elements in either modality. Although our modeling paradigm is general, we explicitly focus on a tabletop building-blocks setting. To test our model, we have acquired a new bimodal dataset comprising images and spoken utterances of colored shapes (blocks) in the tabletop setting. Our experiments demonstrate the benefits of explicitly leveraging compositionality in both quantitative and human evaluation studies.

Farrag, Sara, Alexan, Wassim, Hussein, Hisham H..  2019.  Triple-Layer Image Security Using a Zigzag Embedding Pattern. 2019 International Conference on Advanced Communication Technologies and Networking (CommNet). :1–8.
This paper proposes a triple-layer, high capacity, message security scheme. The first two layers are of a cryptographic nature, whereas the third layer is of a steganographic nature. In the first layer, AES-128 encryption is performed on the secret message. In the second layer, a chaotic logistic map encryption is applied on the output of the first secure layer to increase the security of the scheme. In the third layer of security, a 2D image steganography technique is performed, where the least significant bit (LSB) -embedding is done according to a zigzag pattern in each of the three color planes of the cover image (i.e. RGB). The distinguishing feature of the proposed scheme is that the secret data is hidden in a zigzag manner that cannot be predicted by a third party. Moreover, our scheme achieves higher values of peak signal to noise ratio (PPSNR), mean square error (MSE), the structural similarity index metric (SSIM), normal cross correlation (NCC) and image fidelity (IF) compared to its counterparts form the literature. In addition, a histogram analysis as well as the high achieved capacity are magnificent indicators for a reliable and high capacity steganographic scheme.
Wu, Zhengze, Zhang, Xiaohong, Zhong, Xiaoyong.  2019.  Generalized Chaos Synchronization Circuit Simulation and Asymmetric Image Encryption. IEEE Access. 7:37989–38008.
Generalized chaos systems have more complex dynamic behavior than conventional chaos systems. If a generalized response system can be synchronized with a conventional drive system, the flexible control parameters and unpredictable synchronization state will increase significantly. The study first constructs a four-dimensional nonlinear dynamic equation with quadratic variables as a drive system. The numerical simulation and analyses of the Lyapunov exponent show that it is also a chaotic system. Based on the generalized chaos synchronization (GCS) theory, a four-dimensional diffeomorphism function is designed, and the corresponding GCS response system is generated. Simultaneously, the structural and synchronous circuits of information interaction and control are constructed with Multisim™ software, with the circuit simulation resulting in a good agreement with the numerical calculations. In order to verify the practical effect of generalized synchronization, an RGB digital image secure communication scheme is proposed. We confuse a 24-bit true color image with the designed GCS system, extend the original image to 48-bits, analyze the scheme security from keyspace, key sensitivity and non-symmetric identity authentication, classical types of attacks, and statistical average from the histogram, image correlation. The research results show that this GCS system is simple and feasible, and the encryption algorithm is closely related to the confidential information, which can resist the differential attack. The scheme is suitable to be applied in network images or other multimedia safe communications.
LiPing, Yuan, Pin, Han.  2019.  Research of Low-Quality Laser Security Code Enhancement Technique. 2019 Chinese Automation Congress (CAC). :793–796.
The laser security code has been widely used for providing guarantee for ensuring quality of productions and maintaining market circulation order. The laser security code is printed on the surface of the productions, and it may be disturbed by printing method, printing position, package texture and background, which will make the laser security code cannot work normally. The image enhancement algorithm combining with bilateral filter and contrast limited adaptive histogram equalization is provided, which can realize the enhanced display of laser security code in strong interference background. The performance of this algorithm is analyzed and evaluated by experiments, and it can prove that the indexes of this algorithm are better than others.
Abir, Md. Towsif, Rahman, Lamiya, Miftah, Samit Shahnawaz, Sarker, Sudipta, Al Imran, Md. Ibrahim, Shafiqul Islam, Md..  2019.  Image Encryption and Decryption using Enigma Algorithm. 2019 IEEE International Conference on Electrical, Computer and Communication Technologies (ICECCT). :1—5.

The main objective of this paper is to present a more secured and computationally efficient procedure of encrypting and decrypting images using the enigma algorithm in comparison to the existing methods. Available literature on image encryptions and descriptions are not highly secured in every case.To achieve more secured image processing for highly advanced technologies, a proposed algorithm can be the process used in enigma machine for image encryption and decryption. Enigma machine is piece of spook hardware that was used frequently during the World War II by the Germans. This paper describes the detailed algorithm along with proper demonstration of several essential components present in an enigma machine that is required for image security. Each pixel in a colorful picture can be represented by RGB (Red, Green, Blue) value. The range of RGB values is 0 to 255 that states the red, green and blue intensity of a particular picture.These RGB values are accessed one by one and changed into another by various steps and hence it is not possible to track the original RGB value. In order to retrieve the original image, the receiver needs to know the setting of the enigma. To compare the decrypted image with the original one,these two images are subtracted and their results are also discussed in this paper.

Ravichandran, Dhivya, Fathima, Sherin, Balasubramanian, Vidhyadharini, Banu, Aashiq, Anushiadevi, Amirtharajan, Rengarajan.  2019.  DNA and Chaos Based Confusion-Diffusion for Color Image Security. 2019 International Conference on Vision Towards Emerging Trends in Communication and Networking (ViTECoN). :1–6.
Nowadays, secure transmission of multimedia files has become more significant concern with the evolution of technologies. Cryptography is the well-known technique to safeguard the files from various destructive hacks. In this work, a colour image encryption scheme is suggested using chaos and Deoxyribo Nucleic Acid (DNA) coding. The encryption scheme is carried out in two stages namely confusion and diffusion. As the first stage, chaos aided inter-planar row and column shuffling are performed to shuffle the image pixels completely. DNA coding and decoding operations then diffuse the resultant confused image with the help of eight DNA XOR rules. This confusion-diffusion process has achieved the entropy value equal to 7.9973 and correlation coefficient nearer to zero with key space of 10140. Various other analyses are also done to ensure the effectiveness of the developed algorithm. The results show that the proposed scheme can withstand different attacks and better than the recent state-of-art methods.
Cui, Yongcheng, Wang, Wenyong.  2019.  Colorless Video Rendering System via Generative Adversarial Networks. 2019 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA). :464—467.

In today's society, even though the technology is so developed, the coloring of computer images has remained at the manual stage. As a carrier of human culture and art, film has existed in our history for hundred years. With the development of science and technology, movies have developed from the simple black-and-white film era to the current digital age. There is a very complicated process for coloring old movies. Aside from the traditional hand-painting techniques, the most common method is to use post-processing software for coloring movie frames. This kind of operation requires extraordinary skills, patience and aesthetics, which is a great test for the operator. In recent years, the extensive use of machine learning and neural networks has made it possible for computers to intelligently process images. Since 2016, various types of generative adversarial networks models have been proposed to make deep learning shine in the fields of image style transfer, image coloring, and image style change. In this case, the experiment uses the generative adversarial networks principle to process pictures and videos to realize the automatic rendering of old documentary movies.

Wade, Mamadou I., Chouikha, Mohamed, Gill, Tepper, Patterson, Wayne, Washington, Talitha M., Zeng, Jianchao.  2019.  Distributed Image Encryption Based On a Homomorphic Cryptographic Approach. 2019 IEEE 10th Annual Ubiquitous Computing, Electronics Mobile Communication Conference (UEMCON). :0686–0696.
The objective of this research is to develop a novel image encryption method that can be used to considerably increase the security of encrypted images. To solve this image security problem, we propose a distributed homomorphic image encryption scheme where the images of interest are those in the visible electromagnetic spectrum. In our encryption phase, a red green blue (RGB) image is first separated into its constituent channel images, and then the numerical intensity value of a pixel from each channel is written as a sum of smaller pixel intensity sub-values, leading to having several component images for each of the R, G, and B-channel images. A homomorphic encryption function is used to separately encrypted each of the pixel intensity sub-values in each component image using an encryption key, leading to a distributed image encryption approach. Each of the encrypted component images can be compressed before transmission and/or storage. In our decryption phase, each encrypted component image is decompressed if necessary, and then the homomorphic property of the encryption function is used to transform the product of individually encrypted pixel intensity sub-values in each encrypted component images, to the encryption of their sum, before applying the corresponding decryption function with a decryption key to recover the original pixel's intensity values for each channel image, and then recovering the original RGB image. Furthermore, a special case of an RGB image encryption and decryption where a pixel's intensity value from each channel is written as a sum of only two sub-values is implemented and simulated with a software. The resulting cipher-images are subject to a range of security tests and analyses. Results from these tests shown that our proposed homomorphic image encryption scheme is robust and can resist security attacks, as well as increases the security of the associated encrypted images. Our proposed homomorphic image encryption scheme has produced highly secure encrypted images.
Giełczyk, Agata, Choraś, Michał, Kozik, Rafał.  2018.  Hybrid Feature Extraction for Palmprint-Based User Authentication. 2018 International Conference on High Performance Computing Simulation (HPCS). :629–633.
Biometry is often used as a part of the multi-factor authentication in order to improve the security of IT systems. In this paper, we propose the palmprint-based solution for user identity verification. In particular, we present a new approach to feature extraction. The proposed method is based both on texture and color information. Our experiments show that using the proposed hybrid features allows for achieving satisfactory accuracy without increasing requirements for additional computational resources. It is important from our perspective since the proposed method is dedicated to smartphones and other handhelds in mobile verification scenarios.
Abdolahi, Mahssa, Jiang, Hao, Kaminska, Bozena.  2019.  Robust data retrieval from high-security structural colour QR codes via histogram equalization and decorrelation stretching. 2019 IEEE 10th Annual Ubiquitous Computing, Electronics Mobile Communication Conference (UEMCON). :0340–0346.
In this work, robust readout of the data (232 English characters) stored in high-security structural colour QR codes, was achieved by using multiple image processing techniques, specifically, histogram equalization and decorrelation stretching. The decoded structural colour QR codes are generic diffractive RGB-pixelated periodic nanocones selectively activated by laser exposure to obtain the particular design of interest. The samples were imaged according to the criteria determined by the diffraction grating equation for the lighting and viewing angles given the red, green, and blue periodicities of the grating. However, illumination variations all through the samples, cross-module and cross-channel interference effects result in acquiring images with dissimilar lighting conditions which cannot be directly retrieved by the decoding script and need significant preprocessing. According to the intensity plots, even if the intensity values are very close (above 200) at some typical regions of the images with different lighting conditions, their inconsistencies (below 100) at the pixels of one representative region may lead to the requirement for using different methods for recovering the data from all red, green, and blue channels. In many cases, a successful data readout could be achieved by downscaling the images to 300-pixel dimensions (along with bilinear interpolation resampling), histogram equalization (HE), linear spatial low-pass mean filtering, and gamma function, each used either independently or with other complementary processes. The majority of images, however, could be fully decoded using decorrelation stretching (DS) either as a standalone or combinational process for obtaining a more distinctive colour definition.
Rashid, Rasber Dh., Majeed, Taban F..  2019.  Edge Based Image Steganography: Problems and Solution. 2019 International Conference on Communications, Signal Processing, and Their Applications (ICCSPA). :1–5.

Steganography means hiding secrete message in cover object in a way that no suspicious from the attackers, the most popular steganography schemes is image steganography. A very common questions that asked in the field are: 1- what is the embedding scheme used?, 2- where is (location) the secrete messages are embedded?, and 3- how the sender will tell the receiver about the locations of the secrete message?. Here in this paper we are deal with and aimed to answer questions number 2 and 3. We used the popular scheme in image steganography which is least significant bits for embedding in edges positions in color images. After we separate the color images into its components Red, Green, and Blue, then we used one of the components as an index to find the edges, while other one or two components used for embedding purpose. Using this technique we will guarantee the same number and positions of edges before and after embedding scheme, therefore we are guaranteed extracting the secrete message as it's without any loss of secrete messages bits.

Alia, Mohammad A., Maria, Khulood Abu, Alsarayreh, Maher A., Maria, Eman Abu, Almanasra, Sally.  2019.  An Improved Video Steganography: Using Random Key-Dependent. 2019 IEEE Jordan International Joint Conference on Electrical Engineering and Information Technology (JEEIT). :234–237.

Steganography is defined as the art of hiding secret data in a non-secret digital carrier called cover media. Trading delicate data without assurance against intruders that may intrude on this data is a lethal. In this manner, transmitting delicate information and privileged insights must not rely on upon just the current communications channels insurance advancements. Likewise should make more strides towards information insurance. This article proposes an improved approach for video steganography. The improvement made by searching for exact matching between the secret text and the video frames RGB channels and Random Key -Dependent Data, achieving steganography performance criteria, invisibility, payload/capacity and robustness.

Tan, L., Liu, K., Yan, X., Wan, S., Chen, J., Chang, C..  2018.  Visual Secret Sharing Scheme for Color QR Code. 2018 IEEE 3rd International Conference on Image, Vision and Computing (ICIVC). :961–965.

In this paper, we propose a novel visual secret sharing (VSS) scheme for color QR code (VSSCQR) with (n, n) threshold based on high capacity, admirable visual effects and popularity of color QR code. By splitting and encoding a secret image into QR codes and then fusing QR codes to generate color QR code shares, the scheme can share the secret among a certain number of participants. However, less than n participants cannot reveal any information about the secret. The embedding amount and position of the secret image bits generated by VSS are in the range of the error correction ability of the QR code. Each color share is readable, which can be decoded and thus may not come into notice. On one hand, the secret image can be reconstructed by first decomposing three QR codes from each color QR code share and then stacking the corresponding QR codes based on only human visual system without computational devices. On the other hand, by decomposing three QR codes from each color QR code share and then XORing the three QR codes respectively, we can reconstruct the secret image losslessly. The experiment results display the effect of our scheme.

Wang, Y., Sun, C., Kuan, P., Lu, C., Wang, H..  2018.  Secured graphic QR code with infrared watermark. 2018 IEEE International Conference on Applied System Invention (ICASI). :690–693.

The barcode is an important link between real life and the virtual world nowadays. One of the most common barcodes is QR code, which its appearance, black and white modules, is not visually pleasing. The QR code is applied to product packaging and campaign promotion in the market. There are more and more stores using QR code for transaction payment. If the QR code is altered or illegally duplicated, it will endanger the information security of users. Therefore, the study uses infrared watermarking to embed the infrared QR code information into the explicit QR code to strengthen the anti-counterfeiting features. The explicit graphic QR code is produced by data hiding with error diffusion in this study. With the optical characteristics of K, one of the four printed ink colors CMYK (Cyan, Magenta, Yellow, Black), only K can be rendered in infrared. Hence, we use the infrared watermarking to embed the implicit QR code information into the explicit graphic QR code. General QR code reader may be used to interpret explicit graphic QR code information. As for implicit QR code, it needs the infrared detector to extract its implicit QR code information. If the QR code is illegally copied, it will not show the hidden second QR code under infrared detection. In this study, infrared watermark hidden in the graphic QR code can enhance not only the aesthetics of QR code, but also the anti-counterfeiting feature. It can also be applied to printing related fields, such as security documents, banknotes, etc. in the future.