Deepfake technology poses a serious threat to society by synthesizing a victims facial features and attributes to carry out deception. Traditional active defense methods against deepfake attacks are typically designed for specific models, and protected images often lose their anti-forgery capability after compression or reconstruction, severely limiting their practical applicability. This paper proposes a Meta-Learning-based active defense Scheme against deep facial forgery attacks (MLPDS), which effectively safeguards facial images against diverse deepfake attacks in real-world scenarios. Our approach adopts a general paradigminjecting noise into the original image to construct a cross-model defense algorithm against deepfake attacks. Specifically, by leveraging a meta-learning strategy, we integrate perturbations generated by multiple deepfake models, enabling robust protection against a variety of forgery models. Furthermore, to maintain the high fidelity of the images, we propose a symmetric gradient quantization strategy based on the arctan function to minimize the perceptual discrepancy between the perturbed and original images. Finally, an end-to-end optimization network is employed to generate universal perturbations tailored to specific images, supported by a pixel-level error metric that constrains deviations from the original content. Since no retraining is required to protect newly encountered images, this approach significantly improves the efficiency and practicality of real-time anti-deepfake defense. Experiments show that the proposed MLPDS algorithm can effectively resist attacks from multiple forgery models, outperforming state-of-the-art defense methods and significantly reducing image distortion with an average PSNR gain of approximately 7 dB, which fully meets the practical desire for efficient and reliable deepfake defense.

Existing image steganography schemes always introduce obvious modification traces to the cover image, resulting in the risk of secret information leakage. To address this issue, an end-to-end framework for joint makeup style transfer and image steganography is proposed in this paper to achieve imperceptible higher-capacity data hiding. In the scheme, a Parsing-guided Semantic Feature Alignment (PSFA) module is designed to transfer the style of a makeup image to an object non-makeup image, thereby generating a content-style integrated feature matrix. Meanwhile, a Multi-Scale Feature Fusion and Data Embedding (MFFDE) module was devised to encode the secret image into its latent features and fuse them with the generated content-style integrated feature matrix, as well as the non-makeup image features across multiple scales, to achieve the makeup-stego image. As a result, the style of the makeup image is well transformed and the secret image is imperceptibly embedded simultaneously without directly modifying the pixels of the original non-makeup image. Additionally, a Residual-aware Information Compensation Network (RICN) is developed to compensate the loss of the secret image arising from the multilevel data embedding, thereby further enhancing the quality of the reconstructed secret image. Experimental results show that the proposed scheme achieves superior steganalysis resistance capability and visual quality in both makeup-stego images and recovered secret images, compared with other state-of-the-art schemes.

With the rapid advancement of the Internet of Things (IoT), massive volumes of multimedia data are continuously generated by distributed sensing devices and edge nodes. Efficient and accurate image retrieval from such data has become a key component in enabling advanced IoT applications. However, the constraints of edge computing—including limited bandwidth, low power budgets, and heterogeneous hardware—pose significant challenges to conventional image retrieval schemes. To address these issues, this paper proposes a lightweight and effective Content-Based Image Retrieval (CBIR) framework optimized for cloud-enabled IoT environments. Specifically, this paper introduces a new multi-feature construction scheme that integrates the Color Granular Descriptor (CGD) for fine-grained color characterization, the Double-Radius Local Binary Pattern (DR-LBP) for enhanced local texture extraction, and the Lower-Order Polar Harmonic Fourier Moments (LPHFMs) for capturing global shape features with strong rotational and scale invariance. The proposed scheme achieves high retrieval precision with low computational cost, making it well-suited for deployment in resource-constrained IoT environments. Extensive evaluations conducted on widely used benchmark datasets—including Corel-1K, Corel-5K, Corel-10K, Oxford105K, and GHIM-10K—demonstrate the superior performance and robustness of the proposed method, validating its practical applicability to IoT scenarios.

Perceptual image hashing has emerged as a crucial forensic tool within the Internet of Things (IoT) ecosystem. Traditional perceptual hashing algorithms predominantly rely on global image features to generate hash codes, which limit their ability to represent key features of images effectively. This paper introduces a Perceptual Region Recognition Network (PRRN) to accurately identify key feature regions in images based on their texture distribution characteristics, thereby generating image perceptual hashing codes that reflect the key content of the images. At the same time, a perceptual hashing feature extraction module, which integrates a Residual Network (ResNet) and a Weighted Feature Fusion Network (WFFN), is built to extract deep semantic features of the object image. Where, ResNet is leveraged to extract high-level semantic features, while WFFN ensures the preservation of low-level local features. Furthermore, skip connections are employed to achieve content enhancements for intricate details of critical image regions. Additionally, the Mean Squared Error (MSE) loss is incorporated to enhance the accuracy of key region localization, further improving the sensitivity of image perceptual hash codes and accelerating the network’s convergence speed. Extensive experimental evaluations demonstrate that the proposed PRRN-based perceptual image hashing scheme significantly outperforms other state-of-the-art methods in terms of image feature representation capability. Specifically, it achieves an average improvement of over 1.2 in attack-resistant capability for images compared with other counterparts, making it a promising candidate for practical applications in the IoT environment.

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.

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.

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.

In this paper, based on the principle of Code Division Multiple Access (CDMA), a novel reversible data hiding scheme is presented. The to-be-embedded data are represented by different orthogonal spreading sequences and embedded into a cover image while degrading the image quality slightly. According to the feature of orthogonality, different spreading sequences are repeatedly embedded into the image without disturbing each other, and most elements of different spreading sequences are mutually cancelled in the process of multilevel data embedding. Thus, it keeps the distortion of the embedded image at a relatively low level even with a high embedding capacity. Moreover, the location-map of the proposed scheme can be highly compressed and thus the size is quite small; it further helps to obtain high net embedding capacity. Experimental results have demonstrated that the CDMA based reversible data hiding scheme can achieve higher image quality at the moderate-to-high embedding capacity than other state-of-the-art reversible data hiding works.