With the popularity of personal devices, there are abundant valuable face image datasets in the industry, which provides opportunities for the development of visual models. However, privacy concerns related to identity sensitive information hinder face datasets sharing. Despite existing works dedicated to removing identity sensitive information from images, they either lack provable privacy guarantees or compromise crucial face dataset utilities, e.g., identity correlation and image naturalness. To overcome these weaknesses, we propose a novel face dataset publication scheme that protects face images by obfuscating face features. The obfuscated features still retain a certain level of correlation, allowing the protected dataset to be used for training. In the process of obfuscating the features, we design a novel metric differential privacy mechanism, which can enhance the correlation between features while ensuring privacy. Furthermore, we construct a latent diffusion model with identity and attribute as inputs to improve the naturalness of generated images. Extensive experimental results and theoretical analysis demonstrate our scheme significantly outperforms existing works in providing privacy protection while maintaining high dataset utility for downstream tasks.

Image watermarking is an important technology to protect image content. However, different areas of images, especially the portrait images, are semantically different, and the important areas cannot be protected discriminately. This vulnerability is particularly evident when faced with AI background replacement, which change the whole portrait image except the important face area, and the embedded watermark will be destroyed. To address this issue, this paper proposes a novel semantic-guided watermarking scheme. We apply a semantic local embedding domain selection method to the watermarking model. By using the local guidance module, the watermark information can be embedded in the specific semantic regions of the image, providing discriminative protection for regions of portrait images, while enhancing the watermark’s imperceptibility and robustness. This scheme can effectively address malicious editing scenario of AI background replacement. Even if all regions of the image with low semantic level are tampered with, the watermark information can still be extracted with high precision. Experimental results demonstrate that the proposed scheme achieves excellent imperceptibility, robustness, and extraction accuracy, with a watermark extraction accuracy of 99.9% even under background replacement, validating its feasibility and practicality.

As telemedicine continues to expand, the frequent transmission and sharing of medical images over networks has become central to remote diagnostics. However, these images often contain sensitive lesion information. Once they are illicitly obtained during transmission or storage, they can be misused to train malicious segmentation models, resulting in serious patient privacy violations. While encryption and steganography provide basic protection, encrypted content may draw adversarial attention, and some stego-images may be exposed due to abnormal formats or semantics. More critically, most existing modes lack traceability, making it difficult to identify the source once a privacy breach occurs. To address these challenges, we present a traceable robust adversarial watermarking model that acts as an invisible shield to protect medical image privacy in telemedicine scenarios. This model seamlessly integrates invisibility, privacy protection, and traceability into a unified watermark embedding framework, enabling proactive defense against segmentation-based attacks while maintaining diagnostic quality. Specifically, receiver identity information is embedded into medical images through adversarial perturbations. These perturbations suppress lesion extraction by attackers while allowing reliable identity decoding in case of data leakage. Experimental results show that the model achieves strong privacy protection on various polyp and ISIC datasets. Moreover, the model maintains reliable ID extraction under different noise perturbations, validating its robustness and traceability. Visual quality assessments further confirm the invisibility of the embedded watermark, Ensuring that diagnostic usability remains unaffected. This provides a promising direction for safeguarding medical image privacy in telemedicine environments.

The rapid development of medical information technology promotes the popularization of telemedicine. With remote transmission of patient archives, medical institutions can provide more efficient diagnostic service. Because of the highly sensitive nature of medical data, medical archives typically require the support of encryption to prevent leakage of patient privacy. However, the encrypted archives visually appear as snowflake-like noise, which is easily perceived by an attacker and thus appealing to the crack. In this paper, we propose an imperceptible medical archive construction scheme, which can hide personal medical data in a high-capacity customizable cover image, thus making it impossible for attackers to perceive its presence. Specifically, we first conduct a fusion of multimodal medical image data, integrating image information from various imaging techniques into a unified image, thereby enhancing diagnostic efficacy. Secondly, a high-capacity customizable cover image is generated based on the patient facial mask. Lastly, the unified image with patient demographic information is hidden in the cover image to construct the imperceptible medical archive. To enhance the hiding capacity of the cover image, we propose a Collaborative Steganography Generation Model (CSGM), which increases the low-frequency components during image synthesis, enabling more data to be embedded while maintaining high visual quality. CSGM also supports identity-aware customization using facial structure and semantic text. Experiments show that CSGM improves the PSNR of stego images by 6.95 dB and the PSNR of recovered secret images by 2.98 dB compared to state-of-the-art methods, confirming its effectiveness in imperceptibility and data recovery.

Light field (LF) images capture both spatial and angular data, providing enhanced detail in multi-view and 3D scenes, which makes them highly applicable across various domains. Thus, compared to traditional images, LF images not only contain more complex data and are more susceptible to unauthorized tampering or malicious uses during transmission due to their unique structure, thereby increasing the need for verification. Existing dual watermarking techniques are designed for single-view images and lack adaptability to the multi-view structure and geometric consistency of LF images, making it difficult to simultaneously address integrity verification and ownership verification for LF images. Given the multi-view characteristics and geometric consistency of LF images, there is an urgent need for a highly robust and adaptable dual watermarking method. Therefore, we propose a dual verification framework with customizable watermarking, called DeVIL, which specifically designed for LF images and enables both ownership and integrity verification. By embedding reversible ownership watermarks into the sub-aperture images (SAIs), affiliation can be verified after transmission. Among them, the embedding technique can flexibly choose either a reversible robust watermarking technique or a robust zero-watermarking technique based on different application scenarios. Subsequently, we perform Arnold transformation on key SAIs and embed them into non-key SAIs to create stego images, effectively reducing the risk of information leakage. Furthermore, integrity watermarks are embedded in the stego images, forming stego images with integrity watermarks to detect subtle tampering during transmission. Experimental results show that DeVIL can successfully recover SAIs and demonstrates strong robustness and adaptability against various attacks. Compared to state-of-the-art methods, DeVIL reduces the average bit error rate by 9.32% under different attacks, with average normalized cross-correlation improvement of 0.17, significantly enhancing the robustness of ownership protection in different datasets.

With advancements in the computer industry, 3D objects are now widely used in various applications, including game development, animation production, and industrial design. This growing adoption has increased the need for effective content security and copyright protection for 3D objects. However, existing encryption and watermarking techniques often operate independently, leading to low efficiency and weak coupling between security and copyright protection. To address these gaps, this paper presents a novel method that integrates watermark embedding into the 3D object decryption process, simultaneously ensuring content security and copyright protection. Specifically, a Look-Up Table (LUT)-based encryption method is employed to secure 3D object data, while a Spread Transform Dither Modulation (ST-DM)-based watermarking method is used to embed user-specific identity information during decryption. Unlike conventional approaches that apply encryption and watermarking separately, the proposed method enables efficient 3D object sharing, as the owner only needs to encrypt the object once, regardless of the number of authorized users. The encrypted model can then be distributed securely via multicast and caching. Decryption with personalized keys produces distinct watermarked 3D objects, allowing for reliable traceability of unauthorized redistribution. Experimental results and theoretical evaluations demonstrate that the proposed method delivers satisfactory visual quality, efficiency, robustness, and security.

The task of text-to-image generation has achieved tremendous success in practice, with emerging concept generation models capable of producing highly personalized and customized content. Fervor for concept generation is increasing rapidly among users, and platforms for concept sharing have sprung up. The concept owners may upload malicious concepts and disguise them with non-malicious text descriptions and example images to deceive users into downloading and generating malicious content. The platform needs a quick method to determine whether a concept is malicious to prevent the spread of malicious concepts. However, simply relying on concept image generation to judge whether a concept is malicious requires time and computational resources. Especially, as the number of concepts uploaded and downloaded on the platform continues to increase, this approach becomes impractical and poses a risk of generating malicious content. In this paper, we propose Concept QuickLook, the first systematic work to incorporate malicious concept detection into research, which performs detection based solely on concept files without generating any images. We define malicious concepts and design two operational modes for detection: concept matching and fuzzy detection. Extensive experiments demonstrate that the proposed Concept QuickLook can detect malicious concepts and demonstrate practicality in concept sharing platforms. We also design robustness experiments to further validate the effectiveness of the solution. We hope this work can initiate malicious concept detection tasks and provide some inspiration.

Recently, thumbnail-preserving encryption (TPE) has attracted significant attention because it maintains certain visual information in encrypted images, thereby achieving a trade-off between visual usability and privacy protection. However, existing TPE schemes utilize independent encryption between pixels, blocks, or channels, which results in excessive robustness and consequently introduces security vulnerabilities. To address this issue, we propose a triple-chain architecture to achieve sensitive TPE. This architecture integrates three interdependent encryption chains at the pixel, block, and channel levels, establishing cryptographic dependencies such that any minor modification to either the original or encrypted image leads to completely divergent encryption/decryption outcomes. Specifically, the architecture introduces three chained encryption mechanisms: (1) pixel-level chained encryption propagates encrypted pixels outputs as the part of subsequent inputs; (2) block-level hashing derives new keys from plaintext-key concatenations; and (3) channel-level dependencies are established via iterative key derivation. This cascading structure ensures cryptographic interdependency across all levels. The experimental results verify that the proposed architecture achieves sensitive TPE for the first time while preserving the TPE characteristics of visual usability and privacy protection.

Photoshop inpainting has become one of the most challenging targets in image forensics, as its content-aware and patch-based editing mechanisms produce visually coherent manipulations with weak and localized forensic artifacts. This difficulty is further amplified by JPEG compression, which is routinely introduced during online transmission and tends to suppress the high-frequency tampering traces on which existing forensic detectors largely depend. As a result, current methods face an inherent trade-off: Photoshop-oriented detectors provide strong discriminability under clean conditions but lack robustness to compression, whereas compression-robust forensic methods often fail to capture subtle inpainting artifacts. To overcome this tension, this paper proposes a JPEG-resistant Photoshop inpainting localization method based on multi-frequency representation. The proposed framework employs a set of parameterized frequency-selective filters to extract complementary representations across multiple spectral bands. Each frequency branch is trained independently as a dedicated detector, and a fusion module integrates their outputs to generate a comprehensive localization map that balances discriminability and robustness. A theoretical analysis in the frequency domain is further provided to explain how low-frequency representations remain stable under JPEG-induced attenuation, supporting the design rationale of the proposed framework. In addition, a multi-quality JPEG augmentation strategy is adopted during training to mitigate the mismatch between training and testing compression levels. Extensive experiments on both script-created and hand-created Photoshop inpainting datasets demonstrate that the proposed method consistently outperforms representative forgery localization methods under various JPEG compression strengths. We further evaluate the method on images transmitted through Wechat, Weibo, and Twitter, confirming its effectiveness in practical online social network scenarios. These results demonstrate that the proposed multi-frequency representation strategy offers a principled and effective approach to robust image forensic analysis.

In recent years, a large number of personal images have been uploaded to social network platforms, contributing to the formation of image Big Data. These images are vulnerable to security threats, e.g., privacy inference, copyright infringement, and malicious tampering. Many image protection methods have been proposed, e.g., privacy protection methods based on adversarial perturbations, and copyright protection methods based on data hiding. However, these methods can only deal with a single security threat causing the image to suffer from residual threats. Therefore, this paper proposes a comprehensive image protection framework, which generates adversarial examples by embedding meaningful perturbations, achieving image privacy protection while protecting copyright and integrity by data hiding. In this framework, we design a novel high-capacity adversarial data hiding model (HADH) to support adversarially embedding of adequate robust watermarking for copyright protection and fragile watermarking for integrity protection. Experimental results show that HADH achieves a high embedding rate of 3.21 Reed-Solomon bits per pixel (RS-bpp), providing sufficient capacity for copyright and identity verification information. The capacity is even higher than other pure robust steganography schemes. In addition, the privacy protection performance is better than the existing adversarial attack-based privacy protection methods.

Deep neural networks (DNNs) reveal significant robustness deficiencies due to their susceptibility to being misled by small and imperceptible adversarial examples, thus it is crucial to improve the robustness of DNNs against such harmful perturbations. The current$L_{p}$specification ignores differences in human visual perception when measuring similarity, and most existing image quality assessment (IQA) methods and adversarial example datasets lack subjective scores for evaluation. In this paper, we construct a new database of adversarial examples, called the AED, which contains 35 original images, 1050 adversarial examples, and the corresponding subjective scores of adversarial examples. Then, a novel full-reference IQA model for the quality evaluation of the adversarial examples is proposed by taking into full consideration the hierarchical perception of human visual system (HVS) and the outstanding capabilities of the multi-scale feature extraction network in feature extraction. Specifically, a feature encoding network that uses continuous convolution layers to pre-extract features and expand the receptive field of the image is employed. To simulate the HVS hierarchical perception, the features of different scales are further obtained by designing a multi-scale feature extraction network. The structural similarity scores of the feature maps at different scales are calculated for jointly arriving at the final IQA score of the adversarial examples. Experimental results have demonstrated that our proposed model is closer to the perception of HVS in small imperceptible distortions evaluation of adversarial examples compared with other classical and state-of-the-art models.

The image hosting platform is becoming increasingly popular due to its user-friendly features, but it is prone to causing privacy concerns. Only protecting privacy, in fact, can be easy to come true, but usability is frequently sacrificed. Visual privacy protection schemes aim to make a balance between privacy and usability, whereas they are often irreversible. Recently, some reversible visual privacy protection schemes have been proposed by preserving thumbnails (known as TPE). However, they either have excessive states in the Markov chain modeled by the scheme or cannot reverse losslessly. Meanwhile, images encrypted by existing TPE schemes can not embed additional information and thus the usability is limited to visual observation. In view of this, we pertinently propose a reversible and usability-enhanced visual privacy protection scheme (called PR3) based on thumbnail preservation and data hiding. In this scheme, we utilize the sum-preserving data embedding algorithm to substitute the the lowest seven bits of the image without changing the sum. Any data overflow resulting from the above process is stored in the vacated space of the most significant bits. The remaining space serves two purposes: embedding additional information and adjusting the image to approximate the thumbnail. Compared with existing TPE works, PR3 has fewer states in the Markov chain and supports lossless recovery of images. In addition, additional information can be embedded in the encrypted image to enhance usability.

Nowadays, shared social images often contain multiple privacy subjects, and the disclosure of these privacy subjects increases the risk of privacy violations; thus, the protection of visual privacy is particularly important. However, existing means of visual privacy protection render images unavailable and are typically protected only for images with a single privacy subject. Combining compressed sensing (CS) and 2DCS, this article proposes a multilevel visual privacy protection scheme (MLVPP) via thumbnail preservation (TPE) and key sharing (KS), which includes two stages, that is, CS-TPE multilevel encryption and KS multilevel decryption. In the first stage, we utilize 2DCS to enable the compressed sampled observations to preserve the structural similarity of the nonsensitive part and leverage CS to encrypt multiple sensitive parts of the image. The CS-processed image is then made to strike a good balance between privacy and availability through TPE. In the second stage, with the help of KS mechanism, the CS encryption keys and TPE key are shared in different combinations with the users related to the privacy subjects for meeting the MLVPP requirements. The quality of decrypted images varies for different classes of related users, e.g., unrelated users, semirelated users, and full-related users. The proposed approach preserves the visual information in the nonsensitive part of the image while providing security for the sensitive parts. Compared with related works, MLVPP reveals that the PSNR of the fully decrypted image can reach 34.04, and the SSIM is greater than 0.96 at a compression rate of 0.25. Experimental results demonstrate the superior performance of MLVPP.

Infrared and visible image fusion aims to generate fused images with rich textures and clear target representations. Existing methods generally assume high-quality input images, thus overlooking issues such as reduced contrast and loss of details in visible images under low-light conditions. The naive enhance-then-fuse strategy cannot perform fuse-oriented image enhancement, which always reaches a sub-optimal result. To address this challenge, we propose a perceptual transform fusion of infrared and visible images, which simultaneously optimizes low-light enhancement and image fusion. Specifically, to improve computational efficiency and optimize key feature representations while suppressing noise interactions caused by lighting variations, we introduce a lightweight adaptive sparse Transformer block (ASTBlock). This model adaptively integrates sparse and dense attention mechanisms to enhance feature representations and employs a feed-forward network to eliminate redundant information, thereby ensuring the quality of image fusion. Subsequently, to retain significant details while reducing the impact of noise introduced by low-light enhancement, we incorporate discrete wavelet transform (DWT) for feature decomposition and fusion, further enhancing the representation capability and feature preservation of fused images. Meanwhile, to tackle the issues of insufficient contrast and hidden details in low-light conditions, we design an illumination perception module and an illumination consistency loss to improve the contrast and clarity of fused images. Experimental results on multiple public benchmark datasets for quality assessment and downstream tasks, e.g., pedestrian detection, demonstrate that our method significantly outperforms the state-of-the-art (SOTA) methods. The code is available at https://github.com/hinmouc/PIVFusion.

The prevalence of face capturing along with the advancement of face recognition poses a potential threat to individual privacy. To protect privacy, plenty of methods have been proposed to change identity in the face, thus blocking malicious face recognition. However, these methods fail to satisfy authentication requirements for special application scenarios, e.g., face authentication in surveillance capture. In this paper, we propose a novel face privacy protection model, which supports robust image authentication via information-conditional identity transformation. Specifically, we first introduce a basic face manipulation model (FMM), which can preserve identity-irrelevant attributes when manipulating identity. Based on FMM, we further design a lightweight protector called AIDPro, outputting a transformed identity which is different from the original one and is embedded a message presenting authentication information. Benefiting from the semantic robustness, our model does not require noise layers to achieve accurate message extraction after various image distortions. In addition, the message can be the condition to guide the identity transformation for privacy protection, which avoids extra resource consumption from supporting image authentication. Extensive experimental results demonstrate our model has comparable privacy protection performance, superior attribute preservation performance, and robust authentication performance especially in JPEG compression and screen shooting. Our code is available athttps://github.com/daizigege/AIDPro.

With the rapid proliferation of digital image content and advancements in image editing technologies, the protection of digital image authorship has become an increasingly important issue. Traditional methods for authorship protection include registering authorship through certification organization, utilizing image metadata such as Exchangeable Image File Format (EXIF) data, and employing watermarking techniques to prove ownership. In recent years, blockchain-based technologies have also been introduced to enhance authorship protection further. However, these approaches face challenges in balancing four key attributes: strong legal validity, high security, low cost, and high usability. Authorship registration is often cumbersome, EXIF metadata can be easily extracted and tampered with, watermarking techniques are vulnerable to various forms of attack, and blockchain technology is complex to implement and requires long-term maintenance. In response to these challenges, this paper introduces a new framework Hard EXIF, designed to balance these multiple attributes while delivering improved performance. The proposed method integrates metadata with physically unclonable functions (PUFs) for the first time, creating unique device fingerprints and embedding them into images using watermarking techniques. By leveraging the security and simplicity of hash functions and PUFs, this method enhances EXIF security while minimizing costs. Experimental results demonstrate that the Hard EXIF framework achieves an average peak signal-to-noise ratio (PSNR) of 42.89 dB, with a similarity of 99.46% between the original and watermarked images, and the extraction error rate is only 0.0017. These results show that the Hard EXIF framework balances legal validity, security, cost, and usability, promising authorship protection with great potential for wider application.

In this paper, we propose an audio encryption scheme that supports differentiated decryption, called AES-AUDIO, in which an audio only needs to be encrypted once and can be decrypted into different resolutions as needed. First, we design four security levels, confidential, harsh, noisy, and clear, based on the audio resolution perceived by human auditory perception. Second, the audio data in decimal floating-point numbers (D-FPNs) are unfolded to 32 bits (B-FPNs). Third, we design a region of interest (RoI) encryption algorithm for the audio with the B-FPN format, where the result preserves some perceptual information as needed. Fourth, we construct the AES-AUDIO scheme based on the RoI encryption algorithm, which allows the audio to be encrypted once and then decrypted into different security levels. It supports changing parameters to alter the perception effect corresponding to the security level. Overall, it achieves a balance between the security and usability of the protected audio. User experiments verify that the audios produced by differential decryption can achieve the expected security levels. Some security tests also yielded excellent results, such as an NSCR value of 1.

Light field (LF) image data potentially involves a lot of sensitive information about users. Its transmission channel breaches could compromise user privacy and implicate illegal activities. Therefore, the confidentiality and integrity of LF image data transmission are crucial. However, most of the existing watermarking techniques may not adequately consider the confidentiality and integrity authentication of LF image data transmission. To address this problem, this article employs Generic Radial Harmonic Fourier Moments in Radon space (GRHFMR) to propose a robust zero-watermarking scheme of the LF image, called LFIZW-GRHFMR. Specifically, the radial harmonic Fourier coefficients of sub-aperture images (SAIs) of LF image are calculated by taking into full consideration the characteristics of the GRHFMR variable weight basis function, and then the corresponding watermarks are combined to generate zero-watermarks with stronger robustness. For yielding more watermarking information and enhancing security authentication, the 32 key SAIs of LF image at a sampling rate of 0.5 are selected to act with GRHFMR. Meanwhile, the key SAIs are converted as video streaming with high efficiency video coding for transmission to improve the data transmission efficiency. Extensive experimental results demonstrate that the proposed LFIZW-GRHFMR outperforms the latest watermarking methods.

Modern business activities often rely on rewards programs as a common means of incentivizing consumers. However, in certain cases, it can be a lose-lose situation for both consumers and businesses. For consumers, operators may alter or terminate rewards programs. For operators, rewards programs are difficult to be trusted by consumers and can come with many potential costs. The metaverse is perceived as a utopian-style virtual social system, where brands or individuals, called as operators, can establish stores/stops to conduct business activities. That is, there are also rewards programs in the metaverse, and if people simply replicate existing programs, it may lead to the situations mentioned above. To this end, we aim to propose a win-win rewards program in the metaverse to alleviate the situation. It suggests that consumers can obtain items in rewards programs through self-assembly rather than through the redeem controlled by the operator. Operators cannot make modifications to the rewards program once it is launched. Therefore, this rewards program is trustworthy, and operators also divest themselves from the redemption process in the rewards program, thereby reducing many additional costs. Meanwhile, we implement a specific prototype from a technical perspective to match the proposed rewards program in the metaverse. It specifically implements the decomposition of items prepared by the operator into fragments. Once consumers obtain a sufficient number of fragments, they can assemble them into a complete item without the involvement of the operator.

Abstract With the popularity of the media cloud computing industry, individuals and organizations outsource image computation and storage to the media cloud server to reduce the storage burden. Media images usually contain a large amount of private information. To prevent disclosure of privacy of the image owners, media images are encrypted before uploading to the server. However, this operation will greatly limit the utilization of the image for the user, such as content-based image retrieval. We propose an efficient similarity query algorithm with access control based on Bkd-tree in this paper, in which a searchable encryption scheme is designed for similarity image retrieval, and the encrypted image is used to extract image features by a pre-trained CNN model. The Bkd-tree is utilized to generate an index tree for the image features to speed up retrieval and make it faster than linear indexing. Finally, the security performances of the proposed scheme is analyzed and the performance of this scheme is evaluated by experiments. The results show that the security of the image content and image features can be ensured, and it has a shorter retrieval time and higher retrieval efficiency.

In this letter, to better supplement the advantages of features at different levels and improve the feature extraction ability of the network, a novel multi-level feature interaction transformer network (MFITN) is proposed for pansharpening, aiming to fuse multispectral (MS) and panchromatic (PAN) images. In MFITN, a multi-level feature interaction transformer encoding module is designed to extract and correct global multi-level features by considering the modality difference between source images. These features are then fused using the proposed multi-level feature mixing (MFM) operation, which enables features to fuse interactively to obtain richer information. Furthermore, the global features are fed into a CNN-based local decoding module to better reconstruct high-spatial-resolution multispectral (HRMS) images. Additionally, based on the spatial consistency between MS and PAN images, a band compression loss is defined to improve the fidelity of fused images. Numerous simulated and real experiments demonstrate that the proposed method has the optimal performance compared to state-of-the-art methods. Specifically, the proposed method improves the SAM metric by 7.89% and 6.41% compared to the second-best comparison approach on Pléiades and WorldView-3, respectively.

Thumbnail preserving encryption (TPE) images protect privacy while still maintaining visual usability when stored in the cloud. However, existing thumbnail preserving encryption techniques are insufficient in defending against statistical attack. Drawing on the ideas of 2D compressed sensing (2DCS), this letter proposes thumbnail preserving encryption via dual-2DCS fusion, called TPE-DF, which can resist statistical attack. The original image is processed by 2DCS with deterministic binary block diagonal (DBBD) sampling matrix to generate the sampled image. Then, the residual matrix (RM) is then constructed by subtracting the predictive reconstructed values of the sampled image from the original image. The sampled image is processed by dual-2DCS fusion with arbitrary scaling sensing degradation (ASSD) matrix to obtain a carrier image. Then, the bitstream of RM is embedded in the carrier image to finally generate the TPE image. Experimental results show that a reversible TPE scheme with resistance to statistical attack is achieved.

The ever-growing popularity of image sharing underscores the claim for copyright protection. Digital watermarking plays a pivotal role in combating illegal redistribution and safeguarding copyright. This methodology includes two embedding modes, namely owner-side and client-side embedding, with the latter having an advantage in terms of owner-side efficiency. However, all current client-side watermarking schemes overlook the applicability to images with solid-color backgrounds. Building upon existing researches, this letter uses the lookup table method to realize the client-side embedding of watermarking, and puts forward two schemes specifically tailored for images with solid-color backgrounds. Both two schemes achieve global encryption while successfully limiting the residual watermark effects after decryption to the subject matter of the image. Separately, one scheme adopts the principle of spread spectrum, while the other relies on the principle of spread transform dither modulation. These two schemes exhibit distinct performance trade-offs, and through experimental verification, we demonstrate their decent visual performance, robustness, and efficiency.

Images in electronic devices may pose privacy threats since they can capture sensitive information about consumers. Meanwhile, face recognition (FR) systems are widely used, exacerbating these concerns. Some learning-based schemes have been proposed to protect the privacy of facial images. However, consumers might encounter challenges in implementing them due to specific requirements related to computing power and professional background. The non-learning semantic adversarial perturbation schemes address the aforementioned issues, but they are either irreversible or necessitate additional storage space. To this end, we propose a consumer-oriented image transformation scheme that can prevent the recognition of images by FR systems. It offers a consumer-friendly method compared to schemes that need high-performance equipment and specialized knowledge. The proposed scheme is implemented by reversible embedding the key to coefficients during the JPEG compression. The proposed scheme can be operated in general computing devices by ordinary consumers. The experiments demonstrated that facial images can be protected by the proposed scheme.

In recent years, the uploading of massive personal images has increased the security risks, mainly including privacy breaches and copyright infringement. Adversarial examples provide a novel solution for protecting image privacy, as they can evade the detection by deep neural network (DNN)-based recognizers. However, the perturbations in the adversarial examples typically meaningless and therefore cannot be extracted as traceable information to support copyright protection. In this paper, we designed a dual protection scheme for image privacy and copyright via traceable adversarial examples. Specifically, a traceable adversarial model is proposed, which can be used to embed the invisible copyright information into images for copyright protection while fooling DNN-based recognizers for privacy protection. Inspired by the training method of generative adversarial networks (GANs), a new dynamic adversarial training strategy is designed, which allows our model for achieving stable multi-objective learning. Experimental results show that our scheme is exceptionally robust in the face of a variety of noise conditions and image processing methods, while exhibiting good model migration and defense robustness.

With the widespread application of computer vision, the scenarios in terms of visual privacy have become increasingly diverse and meanwhile numerous studies have been conducted to address privacy concerns in these scenarios. However, these studies are individually tailored for specific scenarios, making their layouts challenging to be drawn upon easily. When encountering a new scenario, it takes significant additional efforts to redesign a scheme due to the low referability of previous works. To tackle this issue, we explore commonalities among existing works and propose a generalized framework to meet the demand for visual privacy protection in various scenarios. Our framework is elaborately organized into several crucial steps, including privacy definition, scenario abstraction, algorithm design, and effect evaluation. It serves as a guide for researchers to efficiently design visual privacy protection schemes. In our framework, we establish a unified standard for quantifying privacy and introduce a novel constrained optimization theory to balance privacy and usability, which contributes to a broader understanding of visual privacy protection. Furthermore, we present an instance under the guidance of the framework that can support identity protection and attribute control scenarios through a diffusion-based model. Extensive experimental results demonstrate the effectiveness of our framework.

Despite the various privacy protection methods that are available through medical services platforms, it is still challenging for patients to achieve a desirable level of privacy protection during image sharing. Therefore, this paper proposes a privacy protection mechanism, called PPM-SEM, for the secure sharing of electronic patient records (EPRs) and medical images in telemedicine; it includes two stages: privacy preparation and privacy protection and reconstruction. In the first stage, a dual watermark (i.e., an image watermark) is generated by combining the patient's EPRs with an image, which can be utilized to ensure the security of patient identity data (i.e., a text watermark).Inthe second stage, a modal transformation network is constructed by training the dual watermark together as an additional channel. This network is called watermark-CycleGAN (W-CycleGAN), which can address the privacy and security issues concerning medical images and provide a double protection mechanism for EPRs. Experimental results demonstrate that only the recovery network with the correct key can restore high-quality medical images. In addition, the patient's EPRs can be fully extracted; i.e., 100% accuracy can be maintained. It is noted that the nonpaired recovery network can also recover visually meaningful medical images, thereby realizing privacy protection for patients in telemedicine scenarios.

Visual security index (VSI) represents a quantitative index for the visual security evaluation of perceptually encrypted images. Recently, the research on visual security of encrypted light field (LF) images faces two challenges. One is that the existing perceptually encrypted image databases are often too small, which is easy to cause overfitting in convolutional neural network (CNN). The other is that existing VSI models did not take a full account the intrinsic characteristics of the LF images and highly relied on handcrafted feature extraction. In this article, we construct a new database of perceptually encrypted LF images, called the PE-SLF, which is 2.6 times as big as the existing largest perceptual encrypted image database. Moreover, a novel visual security index (VSI) model is proposed by taking into full consideration the intrinsic spatial-angular characteristics of the LF images and the outstanding capabilities of CNN in feature extraction. First, we exploit CNN to detect the texture and structure features of encrypted sub-aperture images in the spatial domain. Second, we apply the Gabor filter to detect the Gabor feature over the epi-polar plane images in angular domain. Last, the spatial and angular similarity measurements are subsequently calculated for jointly yielding the final visual security score. Experimental results on the constructed PE-SLF demonstrate that the proposed VSI model is closer to the perception of HVS in visual security evaluation of encrypted LF images compared to other classical and state-of-the-art models.

When visual sensor networks (VSNs) enter daily life in society, they not only bring great convenience but also cause people to worry about privacy. Traditional image encryption uses the snowflake effect to protect the privacy, but this can compromise the usability of directly browsing images captured by nodes in VSNs. Recently, thumbnail-preserving encryption (TPE) has been proposed to balance image usability and privacy. However, the Markov chain’s limited connectivity and high time cost, which result in low efficiency, may preclude its application in VSNs. Motivated by this, we propose a novel TPE scheme to protect the image collected in VSNs’ privacy efficiently. We first deeply study the bijective relationship between the three-pixel group and its rank, and then propose a new number method for both, based on which we design the efficient rank mapping. Subsequently, an efficient thumbnail-preserving image encryption via three-pixel rank mapping (E-TPE) is designed, achieving NR security and enhancing the Markov chain’s connectivity. The experiments demonstrate the scheme’s efficiency; that is, it is currently the quickest ideal-TPE scheme with NR security, and a good balance is achieved between usability and privacy.

Massive face images collected in smart surveillance and social networks are vulnerable to malicious access, thus compromising individual privacy. Existing schemes have been able to protect face privacy while preserving a certain level of identifiability, but have different limitations, e.g., the lack of strong transferability or the inability to retain irrelevant attributes. This letter proposes a novel face privacy protection scheme via virtual identity transformation, which guarantees strong privacy protection and high identifiability. We first solve a specific identity mask for the user, which ensures that the identity features extracted only from the user's faces can be approximated to the given virtual identity. Based on it, the identity transformation networks transform the original face into the protected form, which belongs to the virtual identity while retaining irrelevant attributes. Lastly, the virtual identity of the protected face is extracted for face recognition. Adequate experiments show that our scheme has satisfactory privacy protection, high identifiability, and strong transferability.

Recently, the increasing development of social networks has brought about many privacy-related issues. Because users (participants) have different privacy sharing behaviors when multiple parties participate. The way of privacy sharing depends on the wishes of publishers, and users usually cannot control privacy sharing independently. To address this issue, this article proposes an autonomous privacy control and authentication sharing (APCAS) scheme based on quick response (QR) codes in social networks. Using the error correction of QR codes with high-quality images can prevent photographs that are uploaded to the social network from being subjected to lossy operations, which meets the participants’ needs of availability and privacy simultaneously. Combining the superiority of the polynomial-based secret image sharing and the visual secret image sharing can provide single-share certification for both dealer participation and dealer nonparticipation during certification. By comparing advanced image sharing algorithms, the proposed APCAS can restore the secret images one by one according to users’ wishes and can thus effectively mitigate the problem of autonomous privacy control in online sharing. Compared with related methods in recent years in terms of peak signal-to-noise ratio (PSNR), authentication operation, and authentication complexity, the proposed APCAS algorithm provides lossless decryption, lower authentication computation complexity, and no pixel scalability.

The state of the arts (SOTAs) of single image super-resolution always exploit guidance from gradient prior. The fusion of gradient guidance is implemented by channel-wise concatenation followed by a convolutional layer. However, the kernels sharing in spatial positions cannot adaptively tune the effect of gradient guidance for all feature positions. To resolve this problem, a novel network module is proposed to simulate the traditional Joint Trilateral Filter (JTF) by extending the definition domain from pixels to features. Moreover, to improve the efficiency and flexibility, the functions of JTF kernel generation for image features and gradient features are explicitly learned instead of individual kernel weights, e.g., the exponential functions in the traditional JTF. Based on the proposed JTF modules, this paper follows the gradient-guided framework which simultaneously infers high-resolution (HR) image features and HR gradient features within two parallel branches, respectively. Specifically, by treating image features and gradient features as cross guidance to each other, the proposed JTF modules adaptively adjust the fusion patterns for local features via a bi-directional way. By doing so, the quality of image features and gradient features is alternatively enhanced. Compared with SOTAs, the proposed JTF-SISR shows improvement which is evaluated for multiple upsampling scales and degradation modes on 5 synthetic datasets, i.e., Set5, Set14, B100, Urban100 and Manga109, and 1 real dataset, i.e., RealSRSet. The code is public inhttps://github.com/a239xjc/JTF-SISR.

The tremendous progress in deep learning has enabled to extract soft-biometric attributes from faces, which raises privacy concerns over images collected for face recognition. Advances toward attribute privacy have been able to conceal multiple attributes while preserving identity information but suffer from limitations: they 1) only consider a few soft-biometric attributes and 2) fail to support reversibility for attribute privacy preservation. To break these limitations, we design a reversible privacy-preserving scheme for various face attributes, called reversible attribute privacy preservation (RAPP). RAPP benefits from two modules: 1) The attribute obfuscator introduces a stream cipher to determine that special attributes have to be concealed with the user-defined password, which also supports recovering original attributes. 2) The attribute adversarial network is proposed to generate perturbed images that conceal various attributes while retaining the utility of face verification. In addition, when a wrong password is provided, the returned image with wrong attribute classification results still keeps realistic, which confuses an attacker to know whether the recovery is correct. Extensive experiments demonstrate that RAPP enables to conceal various attributes and recover original images while facilitating face verification.

Self-attention based encoder-decoder models achieve dominant performance in image captioning. However, most existing image captioning models (ICMs) only focus on modeling the relation between spatial tokens, while channel-wise attention is neglected for getting visual representation. Considering that different channels of visual representation usually denote different visual objects, it may lead to poor performance in terms of object and attribute words in the captioning sentences generated by the ICMs. In this paper, we propose a novel dual-stream self-attention module (DSM) to alleviate the above issue. Specifically, we propose a parallel self-attention based module that simultaneously encodes visual information from the spatial and channel dimensions. Besides, to obtain channel-wise visual features effectively and efficiently, we introduce a group self-attention block with linear computational complexity. To validate the effectiveness of our model, we conduct extensive experiments on the standard IC benchmarks including MSCOCO and Flickr30k. Without bells and whistles, the proposed model performs new SOTAs containing 135.4 CIDEr score on MSCOCO and 70.8 CIDEr score on Flickr30k.

Perceptual encryption has received widespread attention as a technology for protecting multimedia visual information. In multimedia applications, perceptual encryption only protects a portion of the content without previewing all the information. At present, perceptual encryption is mostly oriented to conventional plain images while few methods aim at visual security measures for perceptually encrypted images. Existing solutions usually adopt well-known quality assessment metrics to measure the visual quality of encrypted images. However, they often exhibit undesired behavior on perceptually encrypted images with low quality. As a typical representation of three-dimensional scenes, light field images record the intensity and direction of light during propagation which is distinct from 2-D images. In this paper, we construct a perceptually encrypted light field image database (PE-LFID) for quality assessment based on 14 reference light field plaintext images. For each scene, we employ four encryption methods, each of which has six levels. Additionally, a novel visual security evaluation method based on PE-LFID is proposed by taking into account the local and global features of the light field images. First, we use a multi-threshold edge detection method to obtain the edge similarity in the spatial domain of the light field image. Afterwards, the epipolar plane image (EPI) generated from the angular domain of the light field image is used to calculate the gradient magnitude similarity, which is expressed as a global feature. Furthermore, the final quality prediction score is calculated by adaptively weighting between local and global features. We conduct extensive experiments on the proposed PE-LFID to assess the performance of classical and state-of-the-art IQA models. The experimental results demonstrate the effectiveness of the proposed method for visual security evaluation of perceptually encrypted light field images, as well as the scalability of PE-LFID.

This paper proposes an effective infrared and visible image fusion method based on a texture conditional generative adversarial network (TC-GAN). The constructed TC-GAN generates a combined texture map for capturing gradient changes in image fusion. The generator in the TC-GAN is designed as a codec structure for extracting more details, and a squeeze-and-excitation module is applied to this codec structure to increase the weight of significant texture information in the combined texture map. The generator loss function is designed by combing the gradient loss and adversarial loss to retain the texture information of the source images. The discriminator brings the texture of the generated image closer to the visible image. To obtain significant texture information from the source images, a multiple decision map-based fusion strategy is proposed using a combined texture map and an adaptive guided filter. Extensive experiments on the public TNO and RoadScene datasets demonstrate that the proposed method is superior to other state-of-the-art algorithms in terms of a subjective evaluation and quantitative indicators.

A tone-mapped image (TMI) obtained from the corresponding high dynamic range (HDR) image induces artifacts and distortion, which might result in the loss of structure information and impaired color. By analyzing the visual characteristics of TMIs, this work proposes a robust blind visual quality evaluation method for TMIs by using gradient and chromatic statistics (VQGC). First, motivated by the perceptual mechanism that the human visual system (HVS) is sensitive to image structure variation, we employ the gradient features to measure structure degradation in TMIs. To predict structure distortion accurately, we compute the gradient magnitude and orientation to measure image structure variation, and the relative gradient magnitude and orientation are also computed to capture microstructure change. Second, the color invariance descriptors are utilized to capture the visual degradation of colorfulness by local binary pattern (LBP) on four chromatic feature maps. Finally, the gradient and chromatic features are combined together as the final quality-aware feature vector, which is applied to assess the perceptual quality of TMIs by support vector regression (SVR). Comparison experiments show that the performance of the proposed method is better than other existing blind quality assessment methods on public databases.

Cloud platforms provide a good stage for storing and sharing big image data for users, although some privacy issues arise. Image encryption technology can prevent privacy leakage and can ensure secure image data sharing on cloud platforms. Hence, in this paper, an unequal encryption scheme based on saliency detection is proposed. First, based on the mechanism of visual perception and the theory of feature integration, the visual attention model is employed to realize the recognition of significant regions and insignificant regions. Then, a dynamic DNA encryption algorithm is proposed to exploit heavyweight encryption for significant regions, while semi-tensor product compressed sensing is introduced to exploit lightweight encryption and compression for insignificant regions. Experimental results demonstrate that the proposed framework can serve to secure big image data services.

In this paper, we propose an effective blind quality assessment method for screen content images (SCIs), called perceptual quality measure by spatial continuity (PQSC). With the center-surround mechanism in the human visual system (HVS), the proposed method extracts the statistical features on chromatic and textural variations in SCIs to measure the visual distortion. First, by considering the chromatic continuity between spatially adjacent pixels, photo-metric invariant chromatic descriptors are extracted as zero-order and first-order features. Second, motivated by the perceptual mechanism that the HVS is sensitive to image texture variation, we employ local ternary pattern operator to effectively depict the spatial continuity of texture. With these extracted chromatic and textural features, we further adopt histogram to compute the statistical chromatic and textural features. Support vector regression (SVR) is used to train the quality prediction model from visual features to human ratings. Experimental results on three public benchmark databases demonstrate that the performance of our method is superior to the current blind image quality assessment methods, even better than some full reference image quality assessment counterparts.

Salient regions usually carry important information in images. Existing feature encryption algorithms aim at extracting edge features as significant information rather than salient regions for encryption purpose. Moreover, most of them protect significant information by transforming the input image into texture-like or noise-like encrypted image which is obviously a visual sign of encrypted image, and thus can be easily attacked. In this paper, we propose a salient regions encryption scheme to generate visually meaningful ciphertext. First, salient regions are efficiently extracted by a saliency detection model in the compressed domain. Then we pre-encrypt these salient regions by a chaos-based encryption algorithm. With optical encryption theory, the pre-encrypted salient regions are finally transformed into a visually meaningful ciphertext. To the best of our knowledge, it is the first time to use salient regions as important visual information for encryption to obtain cipertext in images. The experimental results demonstrate that the salient regions can be largely hidden with the proposed method.