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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.

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 captured under low-light conditions suffer from inevitable degradation leading to the missing global structure and detailed local texture. However, existing methods consider these two components as a single entity or perform a similar convolutional operation, which can yield suboptimal results. In this letter, we propose a dual-branch structure-texture awareness feature interaction network named DFINet to tackle the above problems. First, we generate structure and texture components through the Gaussian operator. Subsequently, we conduct CNN-based and Transformer-based branches to cope with the texture and structure components separately. Among them, we design a Feature Interaction Block that leverages local-global information to enrich features in the encoding phase. Then, we generate queries with the potential structural-texture cues for the Transformer blocks in the decoding phase. Finally, we develop a Fusion Block to progressively integrate cross-layer features from two branches for the reconstruction. Our extensive experiment indicates the proposed method outperforms several representative methods in terms of both visual quality and objective assessment.

People are used to capturing their daily lives with images and uploading them to the cloud for easy archiving. While cloud storage services bring convenience to users, malicious humans and artificial intelligence machines may pose privacy threats to these uploaded images. Thumbnail-preserving encryption (TPE) is a solution for handling privacy threats posed by the usage of cloud storage and it is able to balance the privacy against humans and usability for users. However, the present TPE schemes display certain vulnerabilities when subjected to machine-based attacks and when the size of the thumbnail block is small, the facial recognition (FR) models exhibit strong capability in recognizing the identity within the protected facial images. To this end, we propose an adversarial thumbnail-preserving transform scheme for facial images, which can not only balance privacy and usability but also resist recognition from FR models. The experiments demonstrate that the proposed scheme has a better ability to resist machine recognition compared with existing schemes.

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.

As an increasing number of people tend to upload personal images to cloud platforms such as iCloud, Google Drive, and Baidu Cloud, the issue of image privacy protection in clouds has attracted widespread attention. Thumbnail-preserving encryption (TPE) is a newly proposed technology especially for balancing usability and privacy of images stored in clouds. The TPE-encrypted image does not reveal anything other than the thumbnail and size of the original one. Although many well-designed TPE schemes have been put forward, they almost all operate in the spatial domain and are not compatible with JPEG images. The only two schemes that can be applied in the frequency domain were proposed by Wright et al. and Marohn et al., but both of them have disadvantages and do not consider non-visual usability. In this letter, we propose a usability enhanced TPE scheme especially for JPEG images by utilizing reversible data hiding and image encryption in the frequency domain. We first transform the original image into the frequency domain and select some specific locations for extra information recording in every block. The original coefficient values at the selected locations are then collected and embedded into the rest of the block before encryption. Finally, the extra information is expected to be recorded into the selected locations of every block for non-visual usability. Experiments confirm that the proposed scheme achieves the target of balancing enhanced usability and privacy.