The utilization of Photo Response Non-Uniformity (PRNU) technology has found extensive application in the field of multimedia forensics, particularly in the authentication of the original camera source of an image. However, this technique has also given rise to significant concerns regarding privacy breaches. For instance, adversaries can exploit publicly available images to generate PRNU and subsequently impersonate the owners of the images. In response to these challenges, we propose an algorithm for achieving source device anonymity in widely used JPEG images. The method combines the discrete cosine transform (DCT) with JPEG compression to process the DCT coefficients of an image after inverse quantization. By ensuring the high quality of the processed image, this approach effectively breaks the link between an image and its source camera. Additionally, a reversible data hiding method is employed, enabling the recovery of traceability if necessary. Our algorithm offers several advantages over existing schemes. It operates within the domain of JPEG image compression, maintaining a low time complexity. Additionally, it effectively preserves the visual quality of images and eliminates the typical traceability effects associated with images.
With the rapid advancement of cloud computing, great security concern regarding data storage has been brougth forth. This paper proposes a reversible data hiding algorithm for JPEG images based on Paillier homomorphic encryption. The secret data is embedded by modifying quantized DCT coefficients. The AC coefficients are modified through homomorphic encryption during the embedding process, while the DC coefficients, which significantly impact image quality, are subjected only to Arnold transform. Additionally, an algorithm that can control the distribution range of ciphertext of Paillier homomorphic encryption is frist presented, thereby, minimizing the ciphertext expansion. Extensive experimental results demonstrate that the proposed algorithm achieves larger data embedding capacity, higher security of ciphertext images, and more stable image quality in comparison with other advanced algorithms.
Deep video inpainting can automatically fill in missing content both in spatial and temporal domain. Unfortunately, malicious video inpainting operations can distort media content, making it challenging for viewers to detect inpainting traces due to their realistic visual effects. As a result, the detection of video inpainting has emerged as a crucial research area in video forensics. Several detection models that have been proposed are trained and tested on datasets made by three kinds of inpainting models, but not tested against the latest and better deep inpainting models. To address this, we introduce a novel end-to-end video inpainting detection network, comprising a feature extraction module and a feature learning module. The Feature extraction module is a Bayar layer and the feature learning module is an encoder-decoder module. The proposed approach is evaluated with inpainted videos created by several state-of-the-art deep video inpainting networks. Extensive experiments has proven that our approach achieved better inpainting localization performance than other methods.
Neural networks have been applied in various fields, including steganography (called neural network steganography). The network used for secret data extraction is called the extractor. This paper proposes a neural network steganography scheme using extractor matching. In our scheme, the extractor is a publicly available normal network possessed by the receiver, which is used for conventional intelligent tasks. Sender connects extractor to another neural network (called cover network), and then trains the connected network to guarantee correctly data extraction without decreasing the performance of the original task of cover network. During the process of training, the parameters of extractor remain unchanged. Specifically, these network parameters are obtained using an extraction key. The receiver can correctly extract secret data with the help of correct extraction key, while an incorrect key will fail to extract secret data. The feasibility of our scheme is demonstrated in experiments.
With the advancement of cloud computing, outsourced image processing has become an attractive business model, but it also poses serious privacy risks. Existing privacy-preserving image scaling techniques often use secret sharing schemes or the Paillier cryptosystem to protect privacy. These methods require the collaboration of multiple servers or only support additive operations, which increases the difficulty of data storage and complicates image processing. To address these issues, this paper focuses on cloud-based privacy-preserving image scaling in the encrypted domain. We propose an image scaling scheme based on homomorphic encryption, which allows cloud servers to perform scaling operations on encrypted images using bicubic interpolation. To avoid the high storage and communication costs of per-pixel encryption, we introduce an efficient data encoding method where a single ciphertext contains the information of an entire image. This significantly reduces the storage space and communication overhead with the cloud server, while our scheme supports computational operations in this data format. Our experimental results validate the feasibility of the proposed scheme, which outperforms existing schemes in terms of storage overhead and operational efficiency.
