DiffCoR: Exposing AI-Generated Image by Using Stable Diffusion Model Based on Consistent Representation Learning

In contemporary applications, especially digital forensics, intellectual property protection, and secure image sharing, it is essential to guarantee the security, integrity, and authenticity of digital images. To improve image authentication, this research presents BlockImage, a sophisticated architecture that combines blockchain storage, cryptographic hashing, AI-driven information extraction, and decentralized image retrieval through IPFS. After being refined on the modified CASIA Tampered Image Dataset, a ResNet-50 model outperformed traditional techniques with a tamper detection accuracy of 94.7%. The solution maintains immutable provenance tracking using the Hyperledger Fabric blockchain and effectively identifies modifications using SHA-256 cryptographic hashing. Furthermore, tamper-proof access to images is made possible through decentralised storage through IPFS, guaranteeing an average retrieval time of about 200 ms per image. Comparing experimental assessments to current methods reveals improved security, storage efficiency, and verification capabilities. The BlockImage framework offers a high-performance, scalable way to safeguard digital images from unwanted changes, guaranteeing their reliability and accessibility over time.

Digital image authentication under varying transmission and acquisition conditions is very challenging and efficient watermarking is an effective approach to solve this problem. In this paper, we propose a transform domain approach in YCbCr color space for enhancing the performance of a watermarking system, employing the unique combination of 2-Level Discrete Wavelet Transform (DWT), Discrete Fourier Transform (DFT) and Singular Value Decomposition (SVD). Individual stages of the system are examined and an attempt is made to improve each stage. YCbCr color space is utilized to make use of its decorrelation property in order to improve correlation between original and watermarked image. DWT stage selectively utilizes the horizontal and vertical detail coefficients of the 2-dimensional DWT of an image. The translation variance problem of the DWT is compensated in the following DFT stage, which also exploits the frequency characteristics of the image. Finally SVD is used to embed the watermark data. The method gives better results in terms of increased PSNR values and is able to withstand a variety of image processing attacks.

Denoising diffusion models have shown a powerful capacity for generating high-quality image samples by progressively removing noise. Inspired by this, we present a diffusion-based mesh denoiser that progressively removes noise from mesh. In general, the iterative algorithm of diffusion models attempts to manipulate the overall structure and fine details of target meshes simultaneously. For this reason, it is difficult to apply the diffusion process to a mesh denoising task that removes artifacts while maintaining a structure. To address this, we formulate a structure-preserving diffusion process. Instead of diffusing the mesh vertices to be distributed as zero-centered isotopic Gaussian distribution, we diffuse each vertex into a specific noise distribution, in which the entire structure can be preserved. In addition, we propose a topology-agnostic mesh diffusion model by projecting the vertex into multiple 2-D viewpoints to efficiently learn the diffusion using a deep network. This enables the proposed method to learn the diffusion of arbitrary meshes that have an irregular topology. Finally, the denoised mesh can be obtained via refinement based on 2-D projections obtained from reverse diffusion. Through extensive experiments, we demonstrate that our method outperforms the state-of-the-art mesh denoising methods in both quantitative and qualitative evaluations.

American defendants have the right to the effective assistance of counsel in criminal prosecutions pursuant to the Sixth Amendment to the U.S. Constitution. We examine how the effective assistance of counsel addresses competence and expertise with computer and digital forensics and electronic evidence. There is a floor of competence in modern litigation requiring competence as to the use of computer forensic services, at least in cases where electronic evidence is used. This is an area that is only now developing and which will continue to evolve. Examination of reported United States cases show a significant increase in the referenced use of computer forensics from 2004 through 2010, indicating significant growth in the use of digital forensics. Digital forensics has also been referenced by the courts, thought to a lesser degree. There are also the first reported cases to appear that assert defense counsel was ineffective for not using such counsel, asserting that counsel was not competent in that failure. The numbers of such cases, though, are too small to indicate anything other than the appearance of this concern as a matter required of competent counsel. Nonetheless, it may indicate a growing use and expectation of competence in the use of computer and digital forensic expertise in the analysis of electronic evidence.

Satellite imagery depicts the Earth’s surface remotely and provides comprehensive information for many applications, such as land use monitoring and urban planning. Existing studies on unsupervised representation learning for satellite images only take into account the images’ geographic information, ignoring human activity factors. To bridge this gap, we propose using the Point-of-Interest (POI) data to capture human factors and designing a contrastive learning-based framework to consolidate the representation of satellite imagery with POI information. Besides, we introduce a season-invariant representation learning model on satellite imagery, considering that human factors are mostly unchanging with respect to seasons. An attention model is designed at last to merge the representations from the geographic, seasonal, and POI perspectives adaptively. On the basis of real-world datasets collected from Beijing, 1 we evaluate our method for predicting socioeconomic indicators. The results show that the representation containing POI information outperforms the geographic representation in estimating commercial activity-related indicators. Our proposed attentional framework can estimate the socioeconomic indicators with R 2 of 0.874 and outperforms the baseline methods. Furthermore, we explore the differences in the representations of satellite images with varying socioeconomic statuses. Finally, we investigate the impact of geographic and POI perspective information in the representation learning process, as well as the effect of satellite imagery on various spatial resolutions.

Digital forensics (now and later called digital forensic science) forensic is a branch of science that involves retrieving and testing objects initiated on cardinal procedures. Images and videos need to develop significant data exporters in the current era. Doctor Images is gradually approaching as an approach that affects individuals and changes their feelings and emotions in the light of different occasions. In the present case, the video counterfeit size has expanded with the expansion of malware work on the Internet, which has made it possible for any customer to download and share items, including images, audio, and video on the Internet. Digital image forensics aims to recognize the legitimacy of images by retrieving data about their history. There are two basic problems: the unique source of the image imaging device and the detection of duplicate notes. Image duplication detection techniques are expected to ensure the validity of computerized images lacking past data about the novel image. There are various courses for modifying an image, for example, rearrangement, splitting, and copy moving. These days, due to the promising results of early selections and constantly growing applications, digital image forensics speaks of an attractive selection space for some researchers. This research study is a comprehensive study of image forensic image processing detection and pixel resolution in video frames.

Digital forensics is a part of forensic science that works with the use of digital data generated, saved, and communicated by digital devices as evidence in investigations and judicial actions. It is a growing field in computing that frequently necessitates the intelligent analysis of large amounts of complex data. A form of digital forensics has existed since nearly the invention of computers, however, as digital forensic processes have matured and needs have become more prevalent, forensic capabilities have seen significant advancements in recent years. Rapid advancements in computer science and information technology enable the development of novel techniques and software for digital investigations. Initially, much of the analysis software was unique and proprietary, but over time, specialised analysis software for both the private and governmental sectors became available. Also, it appears that Artificial Intelligence (AI) is an ideal approach for dealing with many of the current problems in digital forensics. It is a well-established branch of modern computer science that may help solve computationally massive or complicated problems in a reasonable amount of time. The goal of this paper is to deliver a high-level overview of digital forensics phases, applications, merits and demerits and widely used software of the domain. The paper also discusses legitimate and legal considerations followed by the scope and role of artificial intelligence for solving complex problems of digital forensics.

With the rise of high-quality forged images on social media and other platforms, there is a need for algorithms that can recognize the originality. Detecting copy-move forgery is essential for ensuring the authenticity and integrity of digital images, preventing fraud and deception, and upholding the law. Copy-move forgery is the act of duplicating and pasting a portion of an image to another location within the same image. To address these issues, we propose two deep learning approaches - one using a custom architecture and the other using transfer learning. We test our method against a number of benchmark datasets and demonstrate that, in terms of accuracy and robustness against various types of image distortions, it outperforms current state-of-the-art methods. Our proposed method has applications in digital forensics, copyright defence, and image authenticity.

Fake news detection for epidemic emergencies via deep correlations between text and images

Digital core technology is increasingly indispensable for the purpose of efficient development of sandstone reservoir in the Turgay basin central south Kazakhstan which could conduct multiple analyses and experiments without damaging the actual rock core, thereby enhancing efficiency, cutting costs, and minimizing resource wastage. The conventional methods for constructing the digital core mainly include high costs, while the construction cost of physical experimental methods is relatively high and the numerical reconstruction methods exhibit lower efficiency. An intelligent digital core construction framework combining multi-source experimental data with generative deep learning algorithms called MSED-GDL is proposed for quick and efficient reconstruction of the digital core. The framework begins with a U-net based autoencoder that encodes three-dimension (3D) images into a compact latent variable space, enhancing computational efficiency. This is followed by micro-seepage simulation and other processing on the 3D core database to gather multimodal data, including porosities, permeabilities, two-dimension (2D) thin section images, etc. A latent diffusion model is then employed to reconstruct high-fidelity 3D digital cores in seconds, capable of handling incomplete multimodal data inputs. To validate the MSED-GDL framework, 10000 digital core samples with the property of one typical sandstone reservoir in the Turgay basin central south Kazakhstan and a resolution of 256x256x256 were generated using Perlin noise and threshold filtering. Meanwhile, an autoencoder was adopted to compress these into a 16x16x16 tensor representation. Subsequently, the seepage simulations yielded multimodal data—comprising section images, mercury intrusion curves, phase permeability curves, permeabilities, etc. in various data forms (images, series, scalars). The latent diffusion model reconstructed the 3D digital cores using reverse diffusion and iterative denoising, achieving a reconstruction with minimal deviation from actual samples. The permeability, porosity, average throat radius, pore throat ratio, pore throat distribution, and relative permeability curve of the reconstructed digital core have strong consistency with multi-source experimental data indicating that the reconstructed three-dimensional digital core, in terms of geological, physical, and geometric, and other parameters that affect underground flow characteristics, has statistical significance similar to the original digital core and can serve as an approximate replacement for the original digital core.

Positron Emission Tomography (PET) has been a commonly used imaging modality in broad clinical applications. One of the most important tradeoffs in PET imaging is between image quality and radiation dose: high image quality comes with high radiation exposure. Improving image quality is desirable for all clinical applications while minimizing radiation exposure is needed to reduce risk to patients. We introduce PET Consistency Model (PET-CM), an efficient diffusion-based method for generating high-quality full-dose PET images from low-dose PET images. It employs a two-step process, adding Gaussian noise to full-dose PET images in the forward diffusion, and then denoising them using a PET Shifted-window Vision Transformer (PET-VIT) network in the reverse diffusion. The PET-VIT network learns a consistency function that enables direct denoising of Gaussian noise into clean full-dose PET images. PET-CM achieves state-of-the-art image quality while requiring significantly less computation time than other methods. Evaluation with normalized mean absolute error (NMAE), peak signal-to-noise ratio (PSNR), multi-scale structure similarity index (SSIM), normalized cross-correlation (NCC), and clinical evaluation including Human Ranking Score (HRS) and Standardized Uptake Value (SUV) Error analysis shows its superiority in synthesizing full-dose PET images from low-dose inputs. In experiments comparing eighth-dose to full-dose images, PET-CM demonstrated impressive performance with NMAE of 1.278±0.122%, PSNR of 33.783±0.824dB, SSIM of 0.964±0.009, NCC of 0.968±0.011, HRS of 4.543, and SUV Error of 0.255±0.318%, with an average generation time of 62 s per patient. This is a significant improvement compared to the state-of-the-art diffusion-based model with PET-CM reaching this result 12× faster. Similarly, in the quarter-dose to full-dose image experiments, PET-CM delivered competitive outcomes, achieving an NMAE of 0.973±0.066%, PSNR of 36.172±0.801dB, SSIM of 0.984±0.004, NCC of 0.990±0.005, HRS of 4.428, and SUV Error of 0.151±0.192% using the same generation process, which underlining its high quantitative and clinical precision in both denoising scenario. We propose PET-CM, the first efficient diffusion-model-based method, for estimating full-dose PET images from low-dose images. PET-CM provides comparable quality to the state-of-the-art diffusion model with higher efficiency. By utilizing this approach, it becomes possible to maintain high-quality PET images suitable for clinical use while mitigating the risks associated with radiation. The code is availble at https://github.com/shaoyanpan/Full-dose-Whole-body-PET-Synthesis-from-Low-dose-PET-Using-Consistency-Model.

Due to limitations such as geographic, physical, or economic factors, collected seismic data often contain missing traces. Traditional reconstruction methods require extensive empirical parameter tuning and perform poorly on large contiguous sections of missing traces. Recent advances in deep learning have led to diffusion models with strong reconstruction capabilities. However, UNet-based diffusion models are computationally intensive and struggle to learn correlations between different traces. To address these challenges, we proposed SeisRDT — a latent diffusion transformer (DiT) based on representation learning for seismic data reconstruction. This uses a new architecture that we proposed, the representation diffusion transformer (RDT) architecture, which incorporates a relative positional bias that is added when calculating attention. It also uses a mask modeling scheme based on representation learning to enhance reconstructed data amplitude distribution consistency. The combination of these features enables the method to have global information awareness of the seismic data set, which is especially beneficial when there are large contiguous gaps in the data. Despite having these advantages that improve the quality of results, the proposed method has smaller computational resource requirements than most of the alternative approaches we compared it against. This is in part achieved due to its use of a pre-trained data compression model that compresses data into a latent space. Experiments on field and synthetic data sets show that our method achieves higher reconstruction accuracy than some existing methods and can handle various complex missing data scenarios. However, as the SeisRDT learns the probability distribution of the training data, it may not generalize well to data sets with significantly different distributions.

Digital Forensics has become an indispensable tool in contemporary cybercrime investigations, which ensures systematic identification, collection, preservation, and presentation of electronic evidence in a legally admissible manner. As cybercrimes continue to escalate, investigative authorities are required to operate across multiple digital domains, including personal devices, mobile platforms, cloud infrastructures, and transnational communication networks, with strict adherence to the law. Digital forensics is important not only because it can recover the deleted or concealed data, but also because it ensures that the evidence remains authentic and untouched throughout the investigation. Due to its dual emphasis on both technology and law, it effectively bridges the gap between technical accuracy and legal accountability. Despite being so advanced in the contemporary world, the field of digital forensics still faces persistent challenges. The extensive reliance on data encryption, rapid technological innovations, and the issue of complex jurisdiction in cross-border investigations complicate and hamper the effective use of digital forensics. Moreover, the escalation of data generated in the digital age raises concerns about privacy, transparency, and the timely administration of justice. Addressing these concerns requires some advanced and well-developed standardized forensic frameworks, legal protocols, and greater international cooperation to harmonize investigative procedures. This paper shall dwell upon the continuous evolution of the role of digital forensics in cybercrimes in the contemporary world. It shall particular ly focus on the foundations, challenges, evidentiary value, and implications for law enforcement and global cybersecurity. It shall also emphasize the strengthening of digital forensics so that harmonious international relations and global cybersecurity can be achieved.

Accurate genome binning is essential for resolving microbial community structure and functional potential from metagenomic data. However, existing approaches—primarily reliant on tetranucleotide frequency (TNF) and abundance profiles—often perform sub-optimally in the face of complex community compositions, low-abundance taxa, and long-read sequencing datasets. To address these limitations, we present MBGCCA, a novel metagenomic binning framework that synergistically integrates graph neural networks (GNNs), contrastive learning, and information-theoretic regularization to enhance binning accuracy, robustness, and biological coherence. MBGCCA operates in two stages: (1) multimodal information integration, where TNF and abundance profiles are fused via a deep neural network trained using a multi-view contrastive loss, and (2) self-supervised graph representation learning, which leverages assembly graph topology to refine contig embeddings. The contrastive learning objective follows the InfoMax principle by maximizing mutual information across augmented views and modalities, encouraging the model to extract globally consistent and high-information representations. By aligning perturbed graph views while preserving topological structure, MBGCCA effectively captures both global genomic characteristics and local contig relationships. Comprehensive evaluations using both synthetic and real-world datasets—including wastewater and soil microbiomes—demonstrate that MBGCCA consistently outperforms state-of-the-art binning methods, particularly in challenging scenarios marked by sparse data and high community complexity. These results highlight the value of entropy-aware, topology-preserving learning for advancing metagenomic genome reconstruction.

Recent advances in whole-slide image (WSI) scanners and computational capabilities have significantly propelled the application of artificial intelligence in histopathology slide analysis. While these strides are promising, current supervised learning approaches for WSI analysis come with the challenge of exhaustively labeling high-resolution slides-a process that is both labor-intensive and timeconsuming. In contrast, self-supervised learning (SSL) pretraining strategies are emerging as a viable alternative, given that they don't rely on explicit data annotations. These SSL strategies are quickly bridging the performance disparity with their supervised counterparts. In this context, we introduce an SSL framework. This framework aims for transferable representation learning and semantically meaningful clustering by synergizing invariance loss and clustering loss in WSI analysis. Notably, our approach outperforms common SSL methods in downstream classification and clustering tasks, as evidenced by tests on the Camelyon16 and a pancreatic cancer dataset. The code and additional details are accessible at https://github.com/wwyi1828/CluSiam.