Under the rapid development of big data and cloud computing, emerging applications have seen significant improvements in efficiency and service quality. Nevertheless, the conflict between data sharing and privacy preservation remains a major obstacle to the advancement of big data technology. Addressing this issue, this study introduces a solution tailored to the big data environment, which achieves privacy protection and auditability in data sharing and processing. This approach separates data ownership, usage, and validation to mitigate privacy breaches and improper computing behaviors. Leveraging blockchain technology, a transparent governance platform is constructed to identify and track illegal data and computing activities. Furthermore, the solution integrates noninteractive zero-knowledge proofs for publicly verifying data consistency and computing validity on the blockchain. Experimental analysis on computational latency, communication costs, and encryption parameters confirms the feasibility and efficacy of this approach.

Median filtering forensics has attracted much attention in recent years. However, most existing methods exploit either spatial domain or frequency domain features, while neglect the correlation between them. Moreover, they often suffer from inadequate generalization in cross-database and cross-parameter. To solve these problems, we propose an attention-based dual-domain fusion network (DDFNet) to fuse spatial domain and frequency domain features in a mutually complementary way. In the spatial domain, we use four types of modules aimed at extracting the pixel residual features. In the frequency domain, the discrete cosine transform (DCT) coefficients are fed into a residual network to capture the frequency residual features. Finally, the features obtained from the two domains are fused by an attention module to mine their latent complementary relationships. Extensive experiments demonstrate that the proposed DDFNet outperforms the state-of-the-art methods in terms of robustness and generalization capacity.

Detecting double JPEG compression with the same quantization matrix is a challenging task in image forensics. To address this problem, in this paper, a novel method is proposed by leveraging the component convergence during repeated JPEG compressions. Firstly, an in-depth analysis of the pipeline in successive JPEG compressions is conducted, and it reveals that the rounding/truncation errors as well as JPEG coefficients tend to converge after multiple recompressions. Based on this fact, the backward quantization error (BQE) is defined, and we find that the ratio of non-zero BQE for single compression is larger than that for double compression. Moreover, to exploit the convergence property of JPEG coefficients, a multi-threshold strategy is designed for capturing the statistics of the number of different JPEG coefficients between two sequential compressions. Finally, the statistical features of the dual components are concatenated into a 15-D vector to detect double JPEG compression. Experimental results demonstrate the efficiency of the proposed method, which outperforms some state-of-the-art schemes.

Detection of inconsistencies of double JPEG artifacts across different image regions is often used to detect local image manipulations, like image splicing, and to localize them. In this paper, we move one step further, proposing an end-to-end system that, in addition to detecting and localizing spliced regions, can also distinguish regions coming from different donor images. We assume that both the spliced regions and the background image have undergone a double JPEG compression, and use a local estimate of the primary quantization matrix to distinguish between spliced regions taken from different sources. To do so, we cluster the image blocks according to the estimated primary quantization matrix and refine the result by means of morphological reconstruction. The proposed method can work in a wide variety of settings including aligned and non-aligned double JPEG compression, and regardless of whether the second compression is stronger or weaker than the first one. We validated the proposed approach by means of extensive experiments showing its superior performance with respect to baseline methods working in similar conditions.

Reconstructing the image processing chain and the parameters used in each step would provide important forensic clues. Many methods have been designed to estimate the primary quality factor of double compressed JPEG images. However, it is still a challenge to address such problem in the presence of resizing. In this paper, we concentrate on this topic by theoretically analysing the Welch Power Spectral Density (PSD) of the DC coefficients histogram of the counter-resized image. According to the analysis, We find that: i) the most prominent peak of PSD not only dependents on the first quality factor, but also relates to the second one, ii) the peak location nonlinearly maps to the quality factor in the first compression. A simple yet efficient method is proposed to estimate the primary quality factor based on the nonlinear mapping and geometric fitting. Experimental results demonstrate the proposed method provides superior performances.

Available model-based techniques for the estimation of the primary quantization matrix in double-compressed JPEG images work only under specific conditions regarding the relationship between the first and second compression quality factors, and the alignment of the first and second JPEG compression grids. In this paper, we propose a single CNN-based estimation technique that can work under a wide range of settings. We do so, by adapting a dense CNN network to the problem at hand. Particular attention is paid to the choice of the loss function. Experimental results highlight several advantages of the new method, including: i) capability of working under very general conditions, ii) improved performance in terms of MSE and Accuracy, especially in the non-aligned case, iii) better spatial resolution due to the ability of providing good results also on small image patches.