This paper proposes an innovative color anti-counterfeiting quick response code system with a robust authentication mechanism, specifically designed to overcome the limitations of conventional digital anti-counterfeiting technologies. The proposed solution offers four distinct advantages: low implementation cost, superior resistance to forgery, seamless compatibility with standard smartphone cameras, and reliable performance under diverse interference conditions. The technical foundation of our approach combines color multi-channel modulation with advanced halftone theory to generate intricate visual patterns that significantly improve counterfeit resistance. These patterns exhibit two essential security properties: heightened sensitivity to duplication attempts and exceptional resilience against forgery techniques. The authentication system employs a sophisticated dual-spectral analysis methodology that effectively integrates wavelet-domain and Fourier-domain techniques. The wavelet-domain component analyzes macroscopic structural features through scale-dependent approximation coefficients, enabling rapid counterfeit detection. Simultaneously, the Fourier-domain component examines microscopic texture characteristics via localized time-frequency analysis, providing additional security verification. Experimental results demonstrate the superior anti-counterfeiting performance of our scheme, particularly in scenarios with diverse types of interference. This method offers a practical and convenient solution for applications such as identity verification, product identification, medicine traceability, and food safety.

Background and Objective: Benign Paroxysmal Positional Vertigo (BPPV) is a common peripheral vestibular disorder that severely impacts patients' quality of life. Traditional diagnostic methods like Electronystagmography (ENG) and Videonystagmography (VNG) suffer from limitations such as being time-consuming and labor-intensive, and their inability to capture rotational nystagmus. To address these shortcomings, this study proposes an intelligent BPPV diagnostic framework based on deep learning, comprising the Quick Positioning System (QuickPOS) for nystagmus localization and a Multi-frame Spatio-Temporal Attention-enhanced Dual-Stream Network (MSTA-DSN). Notably, this study focuses exclusively on canalithiasis-type BPPV, while rare cases of cupulolithiasis located at the ampullary crest are not included in the scope of this work. Methods: This framework tackles the time-consumption issues of existing methods in long video nystagmus analysis and their limitations when applied to small-scale medical datasets. QuickPOS introduces pupil tracking technology with an adaptive eyelid occlusion compensation strategy and automatic key segment extraction technology, enabling efficient extraction of 9-second nystagmus segments from long videos. MSTA-DSN improves upon the dual-stream network architecture by incorporating multi-frame Red, Green, Blue (RGB) input, a temporal attention mechanism (Time), a spatial attention mechanism called the Convolutional Block Attention Module (CBAM), and an enhanced dual-stream feature fusion method, thereby boosting model performance. Results: Experimental results show that, on the internal dataset, the proposed framework achieves 100% accuracy in binary classification (BPPV vs. non-BPPV) and 95% accuracy in BPPV subtype classification. On the external dataset, the model attains an overall accuracy of 90.43% for four-class subtype classification and achieves 100% accuracy for right horizontal semicircular canal BPPV, demonstrating strong robustness and generalization capability across different data sources. Conclusions: It provides more comprehensive and precise support for the diagnosis and treatment of BPPV, while also offering valuable insights and references for diagnostic research on other similar diseases. To facilitate reproducibility and further research, the implementation and pretrained models are publicly available at: https://github.com/17zhongkaowobisheng/BPPV-diagnosis.

The widespread presence of security patterns in modern anti-forgery systems has given rise to an urgent need for reliable smartphone authentication. However, persistent recognition inaccuracies occur because of the inherent degradation of patterns during smartphone capture. These acquisition-related artifacts are manifested as both spectral distortions in high-frequency components and structural corruption in the spatial domain, which essentially limit current verification systems. This paper addresses these two challenges through four key innovative aspects: (1) It introduces a chromatic-adaptive coupled oscillation mechanism to reduce noise. (2) It develops a DFT-domain processing pipeline. This pipeline includes micro-feature degradation modeling to detect high-frequency pattern elements and directional energy concentration for characterizing motion blur. (3) It utilizes complementary spatial-domain constraints. These involve brightness variation for local consistency and edge gradients for local sharpness, which are jointly optimized by combining maximum a posteriori estimation and maximum likelihood estimation. (4) It proposes an adaptive graph-based partitioning strategy. This strategy enables spatially variant kernel estimation, while maintaining computational efficiency. Experimental results showed that our method achieved excellent performance in terms of deblurring effectiveness, runtime, and recognition accuracy. This achievement enables near real-time processing on smartphones, without sacrificing restoration quality, even under difficult blurring conditions.

The utilization of QR codes in commodity anti-counterfeiting is a prevalent phenomenon. Their traceability via smartphones represents an effective authentication method. However, the captured codes are susceptible to misjudgment due to the influence of blur. To address the above issue, this paper proposes an innovative and efficient blind deblurring optimization method. The method first uses the multi-channel pulse enhancement technique to denoise and the spectral double-feature prior to detect blur. Then, the minimum brightness difference prior and the edge gradient prior are defined, and the code is partitioned into several sub-regions based on graphical guidance. Priors are integrated as constraints in both the maximum a posteriori estimation framework and the maximum likelihood estimation framework to estimate the blur kernel for each sub-region. After applying blur kernels to the corresponding sub-regions, a deblurring operation is performed. Finally, the deblurred sub-regions are stitched together to reconstruct a clear code. Experimental results demonstrate that the proposed method significantly improves the deblurring effect and reduces computation time, providing a novel technical approach to enhance the quality and recognition accuracy of code. The proposed method, as presented in this paper, can be generalized and applied to the deblurring of QR codes in a variety of contexts, including identity verification, product identification, medicine traceability, and food safety.

QR codes are widely used in the traceability of commodities, but the QR codes are easy to become blurred during the acquisition process of mobile phones, which affects their normal identification, so it is necessary to deblur them. This paper proposes a sub-regional deblurring method based on prior knowledge of the gradient and intensity of positioning patterns. Firstly, the QR code is divided into four regions according to the positions of three finder positioning patterns and one alignment positioning pattern. The content invariance of the four positioning patterns can avoid the interference of the content to the estimation of the blur kernel, and also take into account the non-uniformity of the QR code blur. Then the gradient and intensity priors are used to estimate the blur kernels of the position patterns in the four regions, and the calculated blur kernels are applied to the corresponding respective regions for deblurring. Finally, the four deblurred regions are stitched together to obtain the entire deblurring image. The experimental results show that the proposed method performs well in terms of deblurring effect and computational time, surpassing similar deblurring methods.

The authenticity identification of anti-counterfeiting codes based on mobile phone platforms is affected by lighting environment, photographing habits, camera resolution and other factors, resulting in poor collection quality of anti-counterfeiting codes and weak differentiation of anti-counterfeiting codes for high-quality counterfeits. Developing an anti-counterfeiting code authentication algorithm based on mobile phones is of great commercial value. Although the existing algorithms developed based on special equipment can effectively identify forged anti-counterfeiting codes, the anti-counterfeiting code identification scheme based on mobile phones is still in its infancy. To address the small differences in texture features, low response speed and excessively large deep learning models used in mobile phone anti-counterfeiting and identification scenarios, we propose a feature-guided double pool attention network (FG-DPANet) to solve the reprinting forgery problem of printing anti-counterfeiting codes. To address the slight differences in texture features in high-quality reprinted anti-counterfeiting codes, we propose a feature guidance algorithm that creatively combines the texture features and the inherent noise feature of the scanner and printer introduced in the reprinting process to identify anti-counterfeiting code authenticity. The introduction of noise features effectively makes up for the small texture difference of high-quality anti-counterfeiting codes. The double pool attention network (DPANet) is a lightweight double pool attention residual network. Under the condition of ensuring detection accuracy, DPANet can simplify the network structure as much as possible, improve the network reasoning speed, and run better on mobile devices with low computing power. We conducted a series of experiments to evaluate the FG-DPANet proposed in this paper. Experimental results show that the proposed FG-DPANet can resist high-quality and small-size anti-counterfeiting code reprint forgery. By comparing with the existing algorithm based on texture, it is shown that the proposed method has a higher authentication accuracy. Last but not least, the proposed scheme has been evaluated in the anti-counterfeiting code blurring scene, and the results show that our proposed method can well resist slight blurring of anti-counterfeiting images.

At present, anti-counterfeiting schemes based on the combination of anti-counterfeiting patterns and two-dimensional codes is a research hotspot in digital anti-counterfeiting technology. However, many existing identification schemes rely on special equipment such as scanners and microscopes; there are few methods for authentication that use smartphones. In particular, the ability to classify blurry pattern images is weak, leading to a low recognition rate when using mobile terminals. In addition, the existing methods need a sufficient number of counterfeit patterns for model training, which is difficult to acquire in practical scenarios. Therefore, an authentication scheme for an anti-counterfeiting pattern captured by smartphones is proposed in this paper, featuring a single classifier consisting of two modules. A feature extraction module based on U-Net extracts the features of the input images; then, the extracted feature is input to a one-class classification module. The second stage features a boundary-optimized OCSVM classification method. The classifier only needs to learn positive samples to achieve effective identification. The experimental results show that the proposed approach has a better ability to distinguish the genuine and counterfeit anti-counterfeiting pattern images. The precision and recall rate of the approach reach 100%, and the recognition rate for the blurry images of the genuine anti-counterfeiting patterns is significantly improved.

This paper proposes an image tampering detection algorithm based on sample guidance and individual camera device's convolutional neural network (CNN) features (SGICD-CF) to address the challenges in the authenticity and integrity of images. Due to the development of the digital image processing technology, which makes image editing and processing, image tampering and forgery easy and lot simplified, thus solving the problem of image tamper detection, to maintain information security. The principle of SGICD-CF assumes that pixels of the pristine image come from a single camera device, but on the contrary, if an image to be tested is spliced by multiple images from different cameras, then the pixels from the multiple camera devices will be detected. SGICD-CF divides the image to be tested into 64 x 64 pixel image patches, extracts the camera-related features and some camera model-related information of image patches by source camera identification network (SCI-Net) which is proposed by us, and obtains the classification confidence degree of the image patch. Furthermore, it determines whether the image patch contains foreign pixels according to the obtained confidence degree and finally determines whether the image was tampered according to the classification results of all the image patches, thus locating the tampered area. However, the experimental results show that SGICD-CF can detect and locate the tampered area of an image accurately and our methods have a better performance than other existing methods. Our algorithm can achieve an average correct rate of 0.855 on the synthetic data set based on Dresden, which is higher than other existing detection methods.

Although deep learning algorithms have addressed the issue of identifying the source camera to a certain extent, developing a straightforward and effective network remains a challenging task. At present, most of the excellent network schemes in source camera identification are deep networks, which heavily rely on the strong feature extraction ability of deep networks. Although deepening network layers has achieved certain results, training a deep convolutional neural network model requires a large dataset, sophisticated hardware and lengthy training time, and there is a waste of resources. To solve the problem of redundant structure and resource waste of deep convolutional neural networks, this paper proposes the SE-BRB module, which we call a new network module based on the residual module and SE module. Based on this, an adaptive dual-branch fusion network (ADF-Net) with a simplified structure is designed to identify the source of digital images. Specifically, the bottleneck residual module can achieve direct backward transfer of shallow features to avoid images being over-compressed and is suitable for capturing weak source features in images; Additionally, the introduction of a channel attention mechanism can increase the weight of effective feature channels in the network and improve network performance. Finally, multiscale camera feature fusion is realized through a dual-branch network structure to further improve the network performance. The accuracy of the model proposed in this paper is 99.33% and 98.78% on the Dresden dataset and the self-built complex dataset, respectively, and the classification accuracy is far ahead of the existing source camera identification methods.

This paper designs a texture-hidden QR code to prevent the illegal copying of a QR code due to its lack of anti-counterfeiting ability. Combining random texture patterns and a refined QR code, the code is not only capable of regular coding but also has a strong anti-copying capability. Based on the proposed code, a quality assessment algorithm (MAF) and a dual feature detection algorithm (DFDA) are also proposed. The MAF is compared with several current algorithms without reference and achieves a 95% and 96% accuracy for blur type and blur degree, respectively. The DFDA is compared with various texture and corner methods and achieves an accuracy, precision, and recall of up to 100%, and also performs well on attacked datasets with reduction and cut. Experiments on self-built datasets show that the code designed in this paper has excellent feasibility and anti-counterfeiting performance.

Due to the proliferation of high-quality copying devices and the significant profits of counterfeit products, it is critical to establish an effective scheme for detecting and preventing the counterfeiting of goods. At present, most anti-faking schemes leave much to be desired in terms of cost, convenience, and ability to facilitate pre-sale authentication. The paper designs a unique textured pattern and proposes a triple anti-counterfeiting authentication (TACA). First, the textured pattern consists of triple encryptions (the first is that the key area of the QR code is covered, the second includes scale and Arnold transformation, and the third involves replacing the black areas of the pattern with random multi-level grayscales), the abundant details in the texture not only effectively conceal information, but also that their distortion will increase. Second, TACA comprises interpretability analysis (IA), spectral feature analysis (SFA), and spot matching analysis (SMA) in a cascaded way. In further detail, IA mainly exploits the positional transformation of individual pixels and the block features of local regions to restore interpretability. SFA uses the low-frequency subgraph of discrete wavelet transform (DWT) at a specified scale to capture macroscopic structural information. SMA is able to capture the detailed information of the pattern by utilizing SURF to detect the peak region rate positions and employing BRISK to accurately describe them before. Finally, this paper investigates the robustness of the proposed anti-counterfeiting scheme under a variety of copying methods (replicating, scanning-printing), capturing devices (smartphones), and attack scenarios (no attack, cropping, noise, blur).

ObjectiveAnti-counterfeiting code can be as a sort of quick response（QR） code-special design. The functions of anti-counterfeiting and traceability of QR code are involved in beyond encoding and decoding. So, the quality of anticounterfeiting code images is highly required for that. Actually, the obtained anti-counterfeiting code image is challenged to be blurred due to camera-derived noise, the relative shooting motion between camera and anti-counterfeiting code, and its errors-defocused. Generally, QR codes-relevant slight blur degradation does not have a great impact on the function-decoded of anti-counterfeiting codes in terms of its own error-modified ability, but the function of authenticity identification is still a challenging issue to be resolved via the restoration for blurred anti-counterfeiting code images. Most of current blind deblurring algorithms are aimed at natural images, which do not make full use of the features of anti-counterfeiting code.The restoration result is not effective and time cost is high as well. To resolve this problem, we develop a blind-deblurring method on the basis of anti-counterfeiting code’s functional patterns.MethodFirst, the blurred anti-counterfeiting code image is converted into grayscale and interpolation-bilinear is used to coordinate its size to 512 × 512 pixels. The intensity and gradient priors of anti-counterfeiting code image are re-identified in terms of its binary features. Intensity-prior means the gray values of clear anti-counterfeiting code image are concentrated between 0 and 255, while anti-counterfeiting code image-blurred are scattered between 0 and 255. Gradient-prior is defined as the difference between adjacent image pixels,which has horizontal and vertical directions. The gradient distribution of clear anti-counterfeiting code image is amongst 0,255 and-255, whereas gradient values of blurred image are scattered between-255 and 255. Then, the entire blurred anti-counterfeiting code image is divided into four blocks with the same size: 1) upper left, 2) lower left, 3) upper right,and 4) lower right. After that, the three image position detection patterns of upper left, lower left and upper right and the correction pattern of lower right are extracted respectively. There are two potentials of block processing as mentioned below.The first one is beneficial for the deblurring-regularized method, which is scale-related temporal optimization. The other one is focused on comparative analysis for estimating the blur kernel of whole image to make deblurring result better in terms of non-uniform blur-melted block processing. Finally, intensity and gradient-priors cost function is as the constraints, and the deblurring problem is decomposed into two subproblems. The clear images and blur kernels of four blocks are generated based on regularization method and numerical method.ResultFirst, we test 100 artificial blur-relevant anti-counterfeiting code images. The updated blur types consist of motion blur, defocus blur and the two blurs-coordinated. To evaluate the performance of those algorithms to be tested, peak signal to noise ratio（PSNR）, structural similarity（SSIM） and the time cost are used as the indicators. The experimental result shows that our algorithm can deal with varied degrees of motion blur and defocus blur. Next, we test 50 blurred anti-counterfeiting code images collected by mobile phones. Natural image quality evaluator（NIQE） is used as the image quality evaluation index. Our average NIQE value is decreased by 3. 02 and the time cost is optimized by 22. 07 s compared to some popular algorithms, including blind image deblurring using patch wise minimal pixels regularization. Furthermore, the details of anti-counterfeiting pattern can be restored well.ConclusionTo guarantee certain deblurring effect and time efficiency, our easy-to-use blind-deblurring optimization of anti-counterfeiting code images is demonstrated.

Print matter authentication based on anti-counterfeiting techniques has received continuously increasing concern from academia and industry. However, the existing printing anti-counterfeiting solutions often have the defects of poor identification experience, high cost, or weak anti-counterfeiting ability, and cannot achieve pre-sale anti-counterfeiting. Therefore, a novel steganography-based pattern for print matter anti-counterfeiting by smartphone cameras is proposed in this study. Firstly, every pixel in the original binary message image (such as QR code) is replaced by a square pixel block with the same binary gray value of 0 or 255 (the first-level expansion). Secondly, the obtained image is encrypted based on the logistic chaotic sequence, and then scrambled by Arnold transform. Lastly, once again every pixel in the generated image is replaced with a square pixel block (the second-level expansion), the size and gray value of which can be set to control the semi-fragile ability to distinguish an originally printed pattern from its illegitimate copy. If the message extracted from the printed pattern through the inverse procedure is complete enough to decode and read, the pattern is assumed to be an original print. Experimental results verify the advancement and effectiveness of the proposed scheme in distinguishing the copied pattern.

Anti-counterfeiting QR codes are widely used in people's work and life, especially in product packaging. However, the anti-counterfeiting QR code has the risk of being copied and forged in the circulation process. In reality, copying is usually based on genuine anti-counterfeiting QR codes, but the brands and models of copiers are diverse, and it is extremely difficult to determine which individual copier the forged anti-counterfeiting code come from. In response to the above problems, this paper proposes a method for copy forgery identification of anti-counterfeiting QR code based on deep learning. We first analyze the production principle of anti-counterfeiting QR code, and convert the identification of copy forgery to device category forensics, and then a Dual-Branch Multi-Scale Feature Fusion network is proposed. During the design of the network, we conducted a detailed analysis of the data preprocessing layer, single-branch design, etc., combined with experiments, the specific structure of the dual-branch multi-scale feature fusion network is determined. The experimental results show that the proposed method has achieved a high accuracy of copy forgery identification, which exceeds the current series of methods in the field of image forensics.

In recent years, source camera identification has become a research hotspot in the field of image forensics and has received increasing attention. It has high application value in combating the spread of pornographic photos, copyright authentication of art photos, image tampering forensics, and so on. Although the existing algorithms greatly promote the research progress of source camera identification, they still cannot effectively reduce the interference of image content with image forensics. To suppress the influence of image content on source camera identification, a multiscale content-independent feature fusion network (MCIFFN) is proposed to solve the problem of source camera identification. MCIFFN is composed of three parallel branch networks. Before the image is sent to the first two branch networks, an adaptive filtering module is needed to filter the image content and extract the noise features, and then the noise features are sent to the corresponding convolutional neural networks (CNN), respectively. In order to retain the information related to the image color, this paper does not preprocess the third branch network, but directly sends the image data to CNN. Finally, the content-independent features of different scales extracted from the three branch networks are fused, and the fused features are used for image source identification. The CNN feature extraction network in MCIFFN is a shallow network embedded with a squeeze and exception (SE) structure called SE-SCINet. The experimental results show that the proposed MCIFFN is effective and robust, and the classification accuracy is improved by approximately 2% compared with the SE-SCINet network.

An image texture was defined in terms of pixel intensities and directionality. However, most of the current texture representation methods did not consider the two key factors simultaneously. To effectively capture the directional and pixel intensity information of texture, in this paper, we propose a novel and robust local descriptor, named locally directional and extremal pattern (LDEP), for texture classification. It extracts directional local difference count pattern (DLDCP) being made up of DLDCP in the odd positions and DLDCP in the even positions to express directional information in the local area in the first place. Furthermore, to acquire the extremum information remained by DLDCP, by concatenating extremum location pattern (ELP), extremum difference pattern (EDP), and extremum compression pattern (ECP) from the sampling points, we extract a neighbors extremum related local pattern (NERLP). The experimental results obtained from four representative texture databases (Prague, Stex, UIUC, Kth-tips2-a, Brodatz, and CUReT) demonstrate that our proposed LDEP descriptor can achieve comparable accurate classification rates in different conditions (rotation, illumination, scale variation, viewpoint variation, and noise) with ten typical texture classification methods.

As the world is becoming more mobile, mobile applications (or apps) are an integral part of our everyday personal and professional lives. Despite their unprecedented utility, these apps can pose serious security risks since a lot of critical or sensitive information is contained in the distributed software. Therefore, preventing a legitimate software from malicious reverse engineering and other white-box attack is a challenging task. Code obfuscation is a commonly used method to protect software. However, most obfuscation methods merely make the control flow of the program complicated rather than hide the inner logic, and then they are often defeated by reverse engineering. In this paper, we present a new generalized approach to code obfuscation that aims at hiding the basic mathematical operations of the program. This approach splits the basic operations into a set of sub-operations that are replaced by the results retrieved from the protected lookup tables. In order to increase the difficulty for attack analysis, we design the random bijection method and structure similarity method to make the control flow of different obfuscated operation indistinguishable from each other. We also implement our proposed obfuscation method on both source code level and binary code level to demonstrate its broad applicability and examine the performance from multiple dimensions.

Color texture representation is an important step in the task of texture classification. Shortest paths was used to extract color texture features from RGB and HSV color spaces. In this paper, we propose to use shortest paths in the HSI space to build a texture representation for classification. In particular, two undirected graphs are used to model the H channel and the S and I channels respectively in order to represent a color texture image. Moreover, the shortest paths is constructed by using four pairs of pixels according to different scales and directions of the texture image. Experimental results on colored Brodatz and USPTex databases reveal that our proposed method is effective, and the highest classification accuracy rate is 96.93% in the Brodatz database.

The local binary pattern (LBP) model is a simple and effective method of texture classification, but it is sensitive to rotational and noisy images. Although many variants of LBP are proposed by scholars, there are still several urgent problems, such as poor noise and rotation immunity. In this paper, we propose a robust texture descriptor, jumping and refined local pattern (JRLP) for texture classification. In particular, we first extract jumping local difference count pattern (JLDCP) consisting of second-order difference count pattern and diagonal difference count pattern to represent the jumping information in a local domain. To capture the detail information left by JLDCP, we extract a refined completed LBP (RCLBP). By concatenating the JLDCP and RCLBP, we build a JRLP-based robust texture descriptor for classification. Experimental results on four representative texture databases (Brodatz, CUReT, UIUC, and VisTex) reveal that our proposed texture classification method is effective and robust for noise, rotation, scale, and illumination variants and outperforms six representative methods.

Considering the limitation that LBP only focuses on the sign feature in extracting the texture feature as well as its low recognition rate, we in this paper propose an extended contrast ratio local binary pattern for texture classification. The extracted features include its sign feature, energy feature and its center pixel feature, which aims at constructing the histogram based on the features of the sign energy center pixel gained before. Then we perform texture classification by employing the Chi-square distance and the nearest neighbor classifier. Experimental results reveal that our proposed method outperforms several representative texture classification methods.