The images transmitted by IoT devices, particularly those used for surveillance or sensor data, are vulnerable to malicious screenshots and unauthorized access, leading to potential privacy breaches. To address this, we propose a multi-stage Singular Value Decomposition (SVD)-based robust data-hiding scheme for JPEG images aimed at mitigating screenshot attacks. The method exploits the decorrelation properties of the Discrete Cosine Transform (DCT) to preprocess the carrier image, facilitating the selection of specific frequency coefficients. These coefficients undergo a dual-stage SVD transformation, where dimensionality reduction reduces the impact of noise from non-critical image regions. Additionally, we optimize Grad-CAM heatmap generation to better align with human visual perception, enabling the identification of stable and reliable feature regions for embedding secret information. This approach ensures that the visual integrity of the carrier image is maintained while preserving the legibility of the embedded information, even under attack.Our method enhances both the visual fidelity of the carrier image and the robustness of the embedded information. Experimental results demonstrate that the proposed scheme outperforms existing methods, achieving at least a 5% improvement in confidential information extraction accuracy and a data extraction rate exceeding 95% across screenshot angles ranging from -40∘ to 40∘. Extensive evaluations confirm the superior performance, efficiency, and security of our method in mitigating screenshot attacks, showcasing its broad applicability to various image formats and resilience to distortions.

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.

Aiming to mitigate image distortion caused by steganography algorithms at high-capacity information embedding and enhance the steganalysis resistance capability of generated stego images, this paper proposes a high-capacity differential steganography algorithm for color images based on multiple adversarial networks. Instead of directly modifying the pixels of the cover image, the algorithm embeds the secret information into the differential plane generated by the two most similar channels of the cover image. Consequently, the distortion of the stego image is minimized while embedding a secret image of the same size. At the same time, the fidelity of the stego and extracted secret images is continually improved through adversarial training between the generator and discriminator in the proposed steganography network. Furthermore, multiple steganalysis networks are parallelly utilized to enhance the steganalysis resistance capability of stego images. In addition, the Lion optimizer is utilized for the first time to improve the convergence speed of the proposed steganographic network. Experimental results show that the comprehensive performance of the proposed algorithm outperforms other state-of-the-art steganography algorithms significantly.

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.

Chaotic image encryption is widely used in the field of information security. This paper proposes a novel chaotic image encryption method with spiral-transform-based fractal sorting matrix (STFSM). First of all, the theory of STFSM with good scrambling effect is introduced, which has good irregularity and iterative. Then, the iterative algorithm and calculation example of STFSM are introduced. STFSM can be used as the map of spatial location transformations to implement the design of image encryption. Based on the complete STFSM theory and iterative algorithm, a chaotic image cryptosystem based on STFSM is proposed to achieve a good image encryption process. To test the security of the proposed algorithm, security tests and analyses such as entropy analysis, correlation analysis, resistance to differential attacks analysis, and robustness analysis are used for the proposed algorithm. The experimental analysis illustrates that the algorithm has a better encryption effect, whether using conventional tests or the attacks simulations described in this paper and can also effectively resist the attacks.

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.

An efficient predictor is crucial for high embedding capacity and low image distortion. In this letter, a ridge regression-based high precision error prediction algorithm for reversible data hiding is proposed. The ridge regression is a penalized least-square algorithm, which solves the overfitting problem of the least-square method. Reversible data hiding based on ridge regression predictor minimizes the residual sum of squares between predicted and target pixels subject to the constraint expressed in terms of the L2-norm. Compared to a least-square-based predictor, the ridge regression-based predictor can obtain more small prediction errors, proving that the proposed method has a higher accuracy. In addition, the eight neighbor pixels of the target pixels and their two different combinations are selected as training and support sets, respectively. This selection scheme further improves the prediction accuracy. Experimental results show that the proposed method outperforms state-of-the-art adaptive reversible data hiding in terms of prediction accuracy and embedding performance.

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.