Digital image watermarking is a critical technology for image copyright protection. The concurrent evolution of watermarking attacks and defenses has spurred rapid advancements in the field. However, watermarking attack methods have lagged behind, often facing two primary challenges: limited watermark removal ability and quality degradation of the attacked image. In this paper, we introduce a Watermarking Attack method based on the Residual Diffusion Model, termed WARDM. Our WARDM treats watermark information as noise and leverages the powerful image reconstruction capabilities of the diffusion model to effectively remove the watermark. Specifically, we construct a Markov chain based on the residuals between the host and watermarked images, and employ reverse propagation to reconstruct the original host image. To optimally balance watermark removal ability and image quality, we incorporate a noise schedule into WARDM that controls both the velocity and intensity of noise at each stage of the Markov chain. Extensive experiments demonstrate the superior performance of WARDM in both watermark removal capability and visual quality preservation, achieving an improvement of 5.39% in PSNR over state-of-the-art methods. Moreover, our method demonstrates strong generalization, effectively executing attacks across a variety of watermarking techniques.

Deepfake detection remains a challenging research topic, especially when the quality of forged images degrades, leading to unreliable detection results. In this paper, we propose a watermarking-based proactive method for robust proactive deepfake detection. First, we embed a watermark into the Fractional-order Quaternion Exponent Moments (FrQEMs) space of the host face image, achieving a balance between imperceptibility and robustness of the watermarking algorithm. Then, we introduce the Frequency Mamba (FreMamba) block to enhance feature extraction by leveraging correlations between frequency-domain subbands, thereby enabling the extraction of more discriminative feature representations. Finally, at the detection stage, we construct a dual-branch framework comprising a watermark extractor and a forgery discriminator. Through knowledge distillation, the watermark extractor guides the forgery discriminator to perceive forgery traces. Specifically, the integrity of the extracted watermark is compromised only when the host image is subjected to a deepfake attack, while conventional attacks do not affect the integrity. Experimental results on benchmark datasets demonstrate that the proposed method achieves superior deepfake detection accuracy. In particular, when images are subjected to conventional attacks, our method surpasses state-of-the-art approaches by more than 5.3% in terms of ACC.

Face deepfake detection is a critical technology for verifying the authenticity of facial media content and has long been a focal point in multimedia forensics. However, existing methods face significant challenges, primarily due to their limited ability to generalize across domains. Consequently, the growing variety of forgery techniques, combined with the degradation of visual quality in forged images, makes reliable detection even more difficult. To address these challenges, we propose WKD, a proactive deepfake detection framework based on Watermarking and Knowledge Distillation. The key insights of WKD are twofold: First, we embed watermark information into the Fractional-order Quaternion Radial Harmonic Fourier Moments (FrQRHFMs) space of the host image, achieving a robust balance between imperceptibility and robustness. Second, we design a dual-task learning framework consisting of a watermark extractor and a forgery discriminator, where learnable Low-Rank Adaptation (LoRA) layers are used to transfer knowledge from the extractor to the discriminator, thereby providing additional clues for deepfake detection. Specifically, the integrity of the watermark is compromised only when the host image undergoes a deepfake forgery, while it remains unaffected by conventional attacks. Experimental results on benchmark datasets demonstrate that WKD achieves state-of-the-art performance in both intra-domain and cross-domain deepfake detection, particularly when images are subjected to various conventional attacks.

Images are widely used in social networks, necessitating efficient and secure transmission, especially in bandwidth-constrained environments. This article aims to develop a color image encryption algorithm that enhances security while optimizing computational efficiency. A novel parallel color image encryption algorithm based on the 2-D logistic-Rulkov neuron map (2D-LRNM) is proposed. In this approach, the three channels of the color image are first separated. Cross-channel information interaction is introduced to form three new channels, which are then processed in parallel. During the encryption process of each channel, a block-wise parallel encryption mechanism is applied, ensuring simultaneous encryption of each block. This block-wise strategy effectively leverages parallel computing resources and balances the task load. To meet the demand for a large number of keystreams during encryption, the 2D-LRNM is introduced. It combines the simplicity and chaotic properties of the Logistic map with the multitimescale dynamics and neurodynamic behaviors of the Rulkov map. By overcoming the dimensional limitations inherent in the single Logistic map, this approach extends the system to a 2-D framework, significantly increasing the complexity of chaotic behavior and improving its unpredictability. Experimental results demonstrate that the proposed encryption algorithm achieves high security and reduces computation time by approximately 83.3%.

The rapid development of deepfake technology poses challenges to face-centered data security. Existing methods primarily focus on how to transfer deepfake detectors from the source domain to the target domain to handle diverse deepfake techniques. In practical application scenarios, it is usually difficult to access the true and false labels of the source domain. In this letter, we introduce a new adaptation framework called Latent Domain Knowledge Distillation (LDKD) for cross-domain deepfake detection. In the proposed framework, we construct a knowledge distillation structure that includes a student network and a teacher network, which are jointly optimized in a coupled manner to facilitate the model's adaptation to the target domain. Furthermore, to improve the quality of pseudo-labels generated by the teacher network, we propose a Fourier Latent Domain Generation Module (FLGM) and a Stochastic Complementary Mask Module (SCMM). The former is used to generate latent domains to bridge domain differences at the image level, while the latter is employed to mine richer contextual cues for the model. Extensive cross-domain experimental results demonstrate that our method achieves state-of-the-art performance, and the model analysis proves the effectiveness of our key components.

Discrete chaotic systems based on memristors exhibit excellent dynamical properties and are more straightforward to implement in hardware, making them highly suitable for generating cryptographic keystreams. However, most existing memristor-based chaotic systems rely on a single memristor. This paper introduces a novel discrete chaotic system employing dual memristors, named the 3D memristive cubic map with dual discrete memristors (3D-MCM). The 3D-MCM system demonstrates richer and more intricate dynamical behaviors compared to its single-memristor counterparts, as verified through bifurcation diagrams, Lyapunov exponent spectra, and complexity analyses. Notably, the system exhibits coexisting attractors, substantially enhancing its dynamical complexity. Hardware implementation of the 3D-MCM attractors confirms its feasibility for industrial applications. To illustrate the system’s potential in encryption tasks, this study integrates the quaternary-based permutation and dynamic emanating diffusion (QPDED-IE) scheme with the 3D-MCM for image encryption. Experimental results demonstrate that the QPDED-IE scheme based on the 3D-MCM exhibits strong diffusion and confusion properties, effectively resisting cryptanalytic attacks.

In this paper, we propose a robust image steganography method utilizing color conversion, leveraging de-colorization and colorization models to achieve covert transmission of secret information. The motivation is to use color conversion of the stego image to conceal steganographic behavior. For the sender, secret information is embedded into the color cover image using a robust embedding algorithm based on quaternion exponent moments. The stego images are then de-colorized to obtain grayscale images, which can be transmitted over public channels. For the receiver, a corresponding colorization network is designed to reconstruct the stego image and extract the secret information. Additionally, an attack module using Gaussian noise is implemented to enhance the robustness of the proposed steganography. Given a color image, its grayscale version can be chosen from various options, making it difficult for attackers to detect steganographic activity as long as the generated grayscale image appears normal and meaningful. Extensive simulation results demonstrate the feasibility and scalability of the proposed steganography method.

Traditional video encryption methods protect video content by encrypting each frame individually. However, in resource-constrained environments, this approach consumes significant computational resources. To overcome this challenge, this paper proposes a novel method called “Encrypt a story (EAS)”, which aims to enhance encryption efficiency by focusing on encrypting specific segments of the video rather than encrypting each frame. The EAS refers to selecting segments in the time dimension of the video that contain important information or key events for encryption. This method leverages video segmentation techniques to focus encryption efforts on continuous key frames, significantly reducing the consumption of computational resources. To address the need for a large number of key streams during the encryption process, this paper proposes a discrete sinusoidal memristive Rulkov neuron map (DSM-RNM). Through attractor analysis, complexity comparison, Lyapunov exponent, and NIST tests, we validated its ability to generate high-performance pseudorandom sequences, which significantly enhances the security of the encryption algorithm. Notably, the DSM-RNM is shown to exhibit a phenomenon of infinitely coexisting attractors. Furthermore, by constructing a digital circuit to capture the attractors of the DSM-RNM, its potential for industrial applications is demonstrated. Evaluation results show that the EAS saves approximately 90% of the time while ensuring security, exhibiting strong practicality and efficiency

Light-field (LF) images contain rich visual information and have broader application scenarios than traditional images. However, their complex structure also makes their copyright protection more challenging. Currently, there are few watermarking schemes suitable for LF images, and most of them fail to restore the original image after embedding the watermark. In addition, geometric attacks remain a difficult problem in the field of LF image watermarking. In this study, we propose a multiple reversible robust LF image watermarking scheme based on code division multiplexing (CDM) and quaternion polar harmonic Fourier moments (QPHFMs). This scheme embeds multiple identical watermarks into the LF macro-pixel image, and the compensation information for information loss caused by watermark embedding is reversibly embedded into the LF sub-aperture images. The watermark can be extracted and the original LF image can be fully recovered if the image has not been attacked. The watermark can be extracted to verify the copyright ownership of the LF image even when the image has been attacked. Experimental results demonstrate that the proposed watermarking scheme is resistant to various attacks and exhibits strong robustness.

Robust video watermarking refers to the ability to extract the originally embedded watermark information from a video even after malicious modifications and attacks. Currently, traditional watermarking methods have the drawback of lacking robustness against multiple watermark attacks simultaneously. Neural network-based approaches have not fully considered the multi-scale features of videos and tend to lose information during the fusion of scale features. Therefore, we propose a video watermarking scheme based on Channel Spatial Attention. Our model can extract feature information at different scales, allowing the watermark to adapt to features of different scales in the video. Through a series of comparative experiments, our method has shown significant improvements over traditional video watermarking methods and deep learning-based video watermarking models.

The popularity of Deepfake technology has raised the challenge of recognizing real and fake faces. While detection methods already exist, most of them are passive forensics and face challenges of generalizability and migration. Currently, some research attempts to protect the original image by priorly inserting invisible information. However, there are still shortcomings in terms of image quality and information robustness due to information embedding, i.e., watermarking. Therefore, we employ the robustness of perceptual hash coding and combine it with information hiding techniques to propose a proactive Deepfake detection solution, referred to as PWPH in this paper. Our approach is simple and efficient: first, the image containing a face is divided into two parts: FA (face area), and NFA (non-face area). A perceptual hash code is generated from the non-face area (NFA). Then, the hash codes are embedded as watermarks into the FA. At the extraction stage, we use the same method as the encoder to retrieve the embedded watermark from FA. The watermark is then compared with the hash code generated from the NFA of the detected image. The extracted watermark is sensitive to distortion and may vanish during Deepfake processing. Experimental results validate that our method, requiring just one encoder and decoder, enables active detection and source tracking. Furthermore, its efficacy in typical Deepfake scenarios such as face swapping and expression reconstruction is confirmed through comparison with prior arts.

Human parsing has attracted considerable research interest due to its broad potential applications in the computer vision community. In this paper, we explore several useful properties, including high-resolution representation, auxiliary guidance, and model robustness, which collectively contribute to a novel method for accurate human parsing in both simple and complex scenes. Starting from simple scenes: we propose the boundary-aware hybrid resolution network (BHRN), an advanced human parsing network. BHRN utilizes deconvolutional layers and multi-scale supervision to generate rich high-resolution representations. Additionally, it includes an edge perceiving branch designed to enhance the fineness of part boundaries. Building on BHRN, we construct a dual-task mutual learning (DTML) framework. It not only provides implicit guidance to assist the parser by incorporating boundary features, but also explicitly maintains the high-order consistency between the parsing prediction and the ground truth. Toward complex scenes: we develop a domain transform method to enhance the model robustness. By transforming the input space from the spatial domain to the polar harmonic Fourier moment domain, the mapping relationship to the output semantic space is highly stable. This transformation yields robust representations for both clean and corrupted data. When evaluated on standard benchmark datasets, our method achieves superior performance compared to state-of-the-art human parsing methods. Furthermore, our domain transform strategy significantly improves the robustness of DTML dramatically in most complex scenes.

This letter proposes a high-performance image steganography scheme based on dual-adversarial networks to enhance the performance of secret message hiding. According to the characteristics of generative adversarial networks, a dual-adversarial steganography scheme is devised to improve both the visual quality and the steganalysis resistance capability of the stego image. In the first adversarial block, the U-net structure is employed to reconstruct the original image as the generated image, and the adversarial noise is imperceptibly embedded into the generated image to produce the adversarial image that is most suitable for data hiding. In the second adversarial block, secret messages are undetectably embedded into the adversarial image under the confrontation of multiple steganalysis networks. Moreover, a multiple-channel attention module is introduced to enhance the performance of the adversarial image and accelerate the convergence speed of the proposed dual-adversarial networks. Additionally, the MSE loss is employed to minimize the divergence between the original and the adversarial image. Experimental results indicate that the average PSNR of the adversarial images reach 41 dB, and the detection probability is 2.79% lower than that of other advanced schemes. The proposed scheme outperforms its counterparts in terms of performance.

Deepfake technology has rapidly evolved and emerged in recent years, posing significant threats to individuals' reputations and security. Although passive detection methods can achieve reasonable accuracy, they still lack proactive defense mechanisms. To address this issue, this letter proposes a proactive detection framework that combines Quaternion Polar Harmonic Fourier Moments (QPHFMs) with Dual-Task Mutual Learning (DTML) framework. Firstly, watermark information is embedded into QPHFMs, ensuring high imperceptibility while enhancing robustness against common attacks. Secondly, DTML is introduced, where the knowledge distilled from watermark detection can facilitate more accurate deepfake detection. Experimental results on benchmark datasets demonstrate that our method surpasses state-of-the-art techniques, delivering exceptional performance in watermark robustness and imperceptibility while simultaneously accomplishing accurate deepfake detection.

Aiming at the problem of most Robust Reversible Data Hiding (RRDH) schemes failing to anti geometric deformation attacks, a new RRDH algorithm based on Polar Harmonic Fourier Moments (PHFMs) is presented in this paper, thereby enhancing both the robustness of the embedded data and perceptual quality of the data-embedded image. Firstly, by leveraging the anti-geometric transformation and high-fidelity features of PHFMs, the image is transformed into its frequency domain for RRDH. Then, a quantitation index modulation (QIM) algorithm is designed to embed secret data into the integer part of PHFMs coefficients. By minimizing the differences between the secret-data-embedded image and the original image, the amount of compensation data is reduced. Meanwhile, a two-dimensional RDH scheme is further adopted to embed the compensation data, thus reducing the distortion of the full data-embedded image. Finally, the robustness of the embedded data and the fidelity of the full data-embedded image are both improved. The combination of PHFMs transformation and two-dimensional RDH enables the proposed RRDH algorithm to achieve high visual quality and strong resistance capability against geometric transformation attacks. Extensive experimental results demonstrate that the proposed RRDH algorithm outperforms other state-of-the-art techniques.

Deep learning based object detection methods have made significant progress in recent years. However, these methods often suffer from a substantial performance drop when domain shifts occur, making it difficult to generalize a source domain trained object detector to a new target domain. To address this problem, we propose an Online Meta Learning Framework (OMLF) for unsupervised domain adaptive object detection. In our proposed framework, we adopt the Polar Harmonic Fourier Moment (PHFM) to generate target-like intermediate data. The purpose is to construct a two-pair framework that learns meta knowledge (i.e. model initial parameters) from the pair of “source-to-intermediate” to assist another pair of “intermediate-to-target”. Moreover, the optimizing process requires a heavy computational load due to triggering higher-order gradients. To alleviate this problem, we introduce a shortest-path update strategy that accelerates optimization. When evaluated on several benchmark adaptation scenarios (i.e. normal-to-foggy weather, cross cameras, synthetic-to-real, and real-to-artistic), our OMLF achieves state-of-the-art results, demonstrating its effectiveness.

Drone-based vehicle detection is a critical task within intelligent transportation systems. The existing methods that rely solely on single visible or infrared modalities often struggle to achieve both precise and robust detection. Effectively integrating cross-modal information to assist in vehicle detection remains a significant challenge. In this article, we propose a mask-guided Mamba fusion (MGMF) method for visible-infrared vehicle detection in aerial scenes. The proposed MGMF framework consists of two key components: the masked regularization constraint module (MRCM) and the state-space fusion module (SSFM). First, in MAEM, we use candidate regions from one modality to cover corresponding regions of intermediate-level features from another modality, while a regularization constraint extracts cross-modal guidance. This design allows cross-modal features focused on vehicle areas to be extracted from both modalities for fusion. Second, in SSFM, we propose mapping cross-modal features into a shared hidden state for interaction. This reduces disparities between the cross-modal features and enhances the representation, enabling better perception of intermodal correlations. When evaluated on the DroneVehicle dataset, our MGMF achieves an 80.24% with respect to mAP, establishing a new benchmark for state-of-the-art performance. Ablation studies further demonstrate the effectiveness of our MAEM and SSFM in enhancing visible-infrared fusion for vehicle detection.

Digital watermarking technology is a method of embedding specific information in digital images and videos, often used for copyright protection and identity verification. However, unscrupulous users may also use this technique to falsify and tamper with data. Therefore, a reliable watermark attack network is needed to detect and remove watermarks embedded by unscrupulous users. In this paper, we propose a watermarking attack network based on feature pyramid attention, which can effectively remove watermarks embedded in digital images and greatly guarantee the image quality of carrier images. The network consists of two main modules: the feature extraction module and the watermark attack module. In the feature extraction module, we use a convolutional neural network and a residual block to extract the features of the image. Then, in the watermarking attack module, we use the pyramid attention mechanism to focus on the important regions in the feature map and apply the attention weights to the watermarking attack operation. To validate the effectiveness of this network, we conducted experiments using a variety of standard data sets. Experimental results show that the network can effectively attack the watermark information embedded in digital images while guaranteeing the quality of the images after the attack. Overall, the watermarking attack network based on feature pyramid attention proposed in this paper is an effective attack with high imperceptibility that can be applied in the field of digital media protection and security in practical scenarios.

The photo response non-uniformity (PRNU) is used to connect an image to its source sensor. In this paper, researchers propose a PRNU anonymity method based on image segmentation to cut the relationship between the image and its source camera. According to the distribution rule of PRNU in the high and low frequency band of the image, the high and low frequency information of the part is also processed differently, which ensures the quality of the output image to a large extent. Experiments on the datasets show that the proposed method can preserve the biometric characteristics of the device while maintaining the anonymity of the device. Comparing with prior art, peak signal to noise ratio (PSNR) and cosine similarity are improved by 1.9 dB and 0.02 points, respectively.

An asynchronous updating Boolean network is employed to simulate and analyze the gene expression of a particular tissue or species, revealing the life activity process from a system perspective to reveal the disease mechanism and treat the disease. Therefore, to ensure the safe transmission of the asynchronous updating Boolean network in the network, we designed an asynchronous updating Boolean network encryption algorithm based on chaos (ABNEA). First, a novel 2D chaotic system (2D-FPSM) is designed. This system has better performance than the classical 2D chaotic system. It is very suitable for cryptographic systems to generate key streams. Second, an encoding rule is designed to convert the asynchronous updating Boolean network to a Boolean matrix and propagate it on the network as an image. The receiver and sender jointly save the encoding rule. Last, to protect the safe propagation of the Boolean network matrix on the network, the method of synchronous scrambling-diffusion is adapted to encrypt the Boolean network matrix based on the 2D-FPSM. Simulation experiments and security analysis show that the average correlation of adjacent pixels of ciphertext are 0.0010, -0.0010, -0.0020, and the average information entropy is 7.9984. The ABNEA can complete the encryption tasks of asynchronously updating Boolean networks and exhibits good security characteristics.

Abstract This paper proposes a Gaussian Distance Intersection over Union (GDIoU) loss function‐based YOLOv4 deep learning network to solve the problem of slow speed and low accuracy insulator location in power facilities health inspection. In the scheme, A GDIoU loss function is designed to accelerate the convergence speed of the YOLOv4 deep learning network; at the same time, the GDIoU loss is added as one part of the network propagation loss, and the insulator's location accuracy is accordingly improved. Moreover, a re‐location scheme for tilt insulators correction is proposed to enhance the location accuracy of the insulators in different spatial angle states. Large amounts of field insulator images were gathered as training and testing samples to evaluate the performance of the proposed scheme. The experimental results have demonstrated that the GDIoU‐based YOLOv4 deep learning network combined with the tilt correction scheme can improve the insulator location speed by three times compared with the peer schemes, and the average precision is increased by 7.37% compared with the naive YOLOv4 network. The performance of the proposed scheme meets the requirement of online insulator location adequately.

Abstract This paper models a networked coupling chaotic system with a fractional‐like local map based on network topology and fractional theory. The proposed model contains more system parameters and a more relaxed coupling relationship. The entropy analysis also shows that the proposed model is more sensitive to the changes in the coupling parameter. Based on this model, an effective image cipher system is designed using universal scrambling and diffusion based on DNA encoding. The proposed method utilizes the generated pseudo‐random signals from the networked chaotic map. The secret keys can be generated in parallel and the security is also improved due to the rise of complexity of the new chaotic map. Analysis and simulations confirm that the new chaotic map as well as the DNA encoding technology improves the effectiveness and robustness of the proposed encryption algorithm.

While existing watermarking attack methods can disturb the correct extraction of watermark information, the visual quality of watermarked images will be greatly damaged. Therefore, a concealed attack based on generative adversarial network and perceptual losses for robust watermarking is proposed. First, the watermarked image is utilized as the input of generative networks, and its generating target (i.e. attacked watermarked image) is the original image. Inspired by the U-Net network, the generative networks consist of encoder-decoder architecture with skip connection, which can combine the low-level and high-level information to ensure the imperceptibility of the generated image. Next, to further improve the imperceptibility of the generated image, instead of the loss function based on MSE, a perceptual loss based on feature extraction is introduced. In addition, a discriminative network is also introduced to make the appearance and distribution of generated image similar to those of the original image. The addition of the discriminative network can remove watermark information effectively. Extensive experiments are conducted to verify the feasibility of the proposed concealed attack method. Experimental and analysis results demonstrate that the proposed concealed attack method has better imperceptibility and attack ability in comparison to the existing watermarking attack methods.

Digital watermarking technology and watermark attack methods are mutually reinforcing and complementary. Currently, traditional watermark attack methods are relatively mature, but these traditional attack methods will inevitably damage the visual quality of original images (OIs). Therefore, this paper proposes a covert attack method called residual dense based imperceptible watermark attack network (RD-IWAN). First, this paper designs a watermark attack residual dense network (WARDN) based on the residual dense network (RDN), which can effectively remove the watermark information in the middle and low frequency features of the watermarked image (WMI). Second, to improve the attack ability of the network, this paper innovatively proposes a progressive preprocessing method based on the information enhancement preprocessing method. Concurrently, to ensure the imperceptibility of this watermark attack method, a comprehensive loss function that combines the perceptual loss and mean square error loss (MSE) of OI and attacked watermarked image (AWMI) is designed in this study. Finally, attack experiments are designed and performed on watermarks with different embedding strengths and sizes. Experimental results show that, compared to traditional attack methods, the watermark attack method proposed in this paper exhibits stronger attack ability and higher imperceptibility.

Some research progress has been made on fractional-order continuous orthogonal moments (FrCOMs) in the past two years. Compared with integer-order continuous orthogonal moments (InCOMs), FrCOMs increase the number of affine invariants and effectively improve numerical stability. However, the existing types of FrCOMs are still very limited, of which all are planar image oriented. No report on stereoscopic images is available yet. To this end, in this paper, FrCOMs corresponding to various types of InCOMs are first deduced, and then, they are combined with trinion theory to construct trinion FrCOMs (TFrCOMs) applicable to stereoscopic images. Furthermore, the reconstruction performance and geometric invariance of TFrCOMs are analyzed theoretically and experimentally. Finally, an application in the stereoscopic image zero-watermarking algorithm is investigated to verify the superior performance of TFrCOMs.

Imbalanced classification exists commonly in practical applications, and it has always been a challenging issue. Traditional classification methods have poor performance on imbalanced data, especially, on the minority class. However, the minority class is usually of our interest, and its misclassification cost is higher. The critical factor is the intrinsic complicated distribution characteristics in imbalanced data itself. Resampling ensemble learning achieves promising results and is a research focus recently. However, some resampling ensembles do not consider complicated distribution characteristics, thus limiting the performance improvement. In this paper, a generalized optimization embedded framework (GOEF) is proposed based on undersampling bagging. The GOEF aims to pay more attention to the learning of local regions to handle the complicated distribution characteristics. Specifically, the GOEF utilizes out-of-bag data to explore heterogeneous local areas and chooses misclassified examples to optimize base classifiers. The optimization can focus on a single class or both classes. Extensive experiments over synthetic and real datasets demonstrate that GOEF with the minority class optimization performs the best in terms of AUC, G-mean, and sensitivity, compared with five resampling ensemble methods.

Medical image data, like most patient information, has high requirements for privacy and confidentiality. To improve the security of medical image transmission within the open network, we proposed a medical image key area protection algorithm based on reversible data hiding. First, the coefficient of variation is used to identify the key area, that is, the lesion area of the image. Then, the other regions are divided into blocks to analyze the texture complexity. Next, we propose a new reversible data hiding algorithm, which embeds the content of the key area into the high-texture regions. On this basis, a quick response (QR) code is generated using the ciphertext of the basic image information to replace the original lesion area. Experimental results show that this method can not only safely transmit sensitive patient information by hiding the content of the lesion, it can also store copyright information through QR code and achieve accurate image retrieval.

Exponential moments (EMs) are important radial orthogonal moments, which have good image description ability and have less information redundancy compared with other orthogonal moments. Therefore, it has been used in various fields of image processing in recent years. However, EMs can only take integer order, which limits their reconstruction and antinoising attack performances. The promotion of fractional-order exponential moments (FrEMs) effectively alleviates the numerical instability problem of EMs; however, the numerical integration errors generated by the traditional calculation methods of FrEMs still affect the accuracy of FrEMs. Therefore, the Gaussian numerical integration (GNI) is used in this paper to propose an accurate calculation method of FrEMs, which effectively alleviates the numerical integration error. Extensive experiments are carried out in this paper to prove that the GNI method can significantly improve the performance of FrEMs in many aspects.

Due to their good rotational invariance and stability, image continuous orthogonal moments are intensively applied in rotationally invariant recognition and image processing. However, most moments produce numerical instability, which impacts the image reconstruction and recognition performance. In this paper, a new set of invariant continuous orthogonal moments, polar harmonic Fourier moments (PHFMs), free of numerical instability is designed. The radial basis functions (RBFs) of the PHFMs are much simpler than those of the Chebyshev-Fourier moments (CHFMs), orthogonal Fourier-Mellin moments (OFMMs), Zernike moments (ZMs), and pseudo-Zernike moments (PZMs). For the same degree, the RBFs of the PHFMs have more zeros and are more evenly distributed than those of the ZMs and PZMs. Therefore, PHFMs do not suffer from information suppression problem; hence, the image description ability of the PHFMs is superior to that of the ZMs and PZMs. Moreover, the RBFs of the PHFMs are always less than or equal to 1.0 near the unit disk center, whereas those of the OFMMs, PZMs, CHFMs, and radial harmonic Fourier moments (RHFMs) are infinite (implying numerical instability). This indicates that PHFMs can outperform these moments in image reconstruction tasks. We theoretically and experimentally demonstrate that PHFMs outperform the above moments in reconstructing images and recognizing rotationally invariant objects considering noise and various attacks. This paper also details the significance of the PHFM phase in image reconstruction, angle estimation using PHFMs, and the accurate moment selection of the PHFMs.

This paper proposes a new medical image super-resolution (SR) network, namely deep multi-scale network (DMSN), in the uniform discrete curvelet transform (UDCT) domain. DMSN is made up of a set of cascaded multi-scale fushion (MSF) blocks. In each MSF block, we use convolution kernels of different sizes to adaptively detect the local multi-scale feature, and then local residual learning (LRL) is used to learn effective feature from preceding MSF block and current multi-scale features. After obtaining multi-scale features of different MSF block, we use global feature fusion (GFF) to jointly and adaptively learn global hierarchical features in a holistic manner. Finally, compared with other prediction methods in spatial domain, we applied DMSN in UDCT domain, which enables a better representation of global topological structure and local texture detail of HR images. DM-SN shows superior performance over other state-of-the-art medical image SR methods.

In computer vision, the analysis of image contents plays a significant role to perform intelligent tasks such as object recognition and image retrieval. These contents can be low‐level visual features or colour information within an image. For content‐based image retrieval (CBIR), several methods have been proposed that focus on either low‐level visual features extraction or the colour information, and very few works can be seen that retrieve the images by fusing both types of contents. Consequently, this work addresses the problem of combining low‐level visual features with colour information that helps to improve the retrieval accuracy of CBIR. The proposed strategy extracts the low‐level visual salient features with features from accelerated segment test feature descriptor and quantises the salient keypoints into a feature vector. The colour information of the image is extracted and segmented with non‐linear L * a * b * colour space and quantised into a feature vector. The similarity for both the feature vectors including visual and colour features is computed and combined together. The top‐rank images are retrieved for the obtained feature vector using the distance metric. The experimental results on two standard benchmark datasets show the improved efficiency and 85% accuracy of the proposed strategy over state‐of‐the‐art methods.

This work addresses the problem of searching and retrieving similar textual images based on the detected text and opens the new directions for textual image retrieval. For image retrieval, several methods have been proposed to extract visual features and social tags; however, to extract embedded and scene text within images and use that text as automatic keywords/tags is still a young research field for text‐based and content‐based image retrieval applications. The automatic text detection retrieval is an emerging technology for robotics and artificial intelligence. In this study, the authors have proposed a novel approach to detect the text in an image and exploit it as keywords and tags for automatic text‐based image retrieval. First, text regions are detected using maximally stable extremal region algorithm. Second, unwanted false positive text regions are eliminated based on geometric properties and stroke width transform. Next, the true text regions are proceeded into optical character recognition for recognition. Third, keywords are formed using a neural probabilistic language model. Finally, the textual images are indexed and retrieved based on the detected keywords. The experimental results on two benchmark datasets show the dominancy of text is efficient and valuable for image retrieval specifically for textual images.

In this paper, a new reversible data hiding (RDH) scheme based on Code Division Multiplexing (CDM) and machine learning algorithms for medical image is proposed. The original medical image is firstly converted into frequency domain with integer-to-integer wavelet transform (IWT) algorithm, and then the secret data are embedded into the medium frequency subbands of medical image robustly with CDM and machine learning algorithms. According to the orthogonality of different spreading sequences employed in CDM algorithm, the secret data are embedded repeatedly, most of the elements of spreading sequences are mutually canceled, and the proposed method obtained high data embedding capacity at low image distortion. Simultaneously, the to-be-embedded secret data are represented by different spreading sequences, and only the receiver who has the spreading sequences the same as the sender can extract the secret data and original image completely, by which the security of the RDH is improved effectively. Experimental results show the feasibility of the proposed scheme for data embedding in medical image comparing with other state-of-the-art methods.