Towards Effective Image Forensics via A Novel Computationally Efficient Framework and A New Image Splice Dataset

Splice detection models are the need of the hour since splice manipulations can be used to mislead, spread rumors and create disharmony in society. However, there is a severe lack of image splicing datasets, which restricts the capabilities of deep learning models to extract discriminative features without overfitting. This manuscript presents two-fold contributions toward splice detection. Firstly, a novel splice detection dataset is proposed having two variants. The two variants include spliced samples generated from code and through manual editing. Spliced images in both variants have corresponding binary masks to aid localization approaches. Secondly, a novel Spatio-Compression Lightweight Splice Detection Framework is proposed for accurate splice detection with minimum computational cost. The proposed dual-branch framework extracts discriminative spatial features from a lightweight spatial branch. It uses original resolution compression data to extract double compression artifacts from the second branch, thereby making it 'information preserving.' Several CNNs are tested in combination with the proposed framework on a composite dataset of images from the proposed dataset and the CASIA v2.0 dataset. The best model accuracy of 0.9382 is achieved and compared with similar state-of-the-art methods, demonstrating the superiority of the proposed framework

3D Visualization Architecture for Building Applications Leveraging an Existing Validated Toolkit

The diagnostic radiology space and healthcare in general is a slow adopter of new software technologies and patterns. Despite the widespread embrace of mobile technology in recent years, altering the manner in which societies in developed countries live and communicate, diagnostic radiology has not unanimously adopted mobile technology for remote diagnostic review. Desktop applications in the diagnostic radiology space commonly leverage a validated toolkit. Such toolkits not only simplify desktop application development but minimize the scope of application validation. For these reasons, such a toolkit is an important piece of a company’s software portfolio. This thesis investigated an approach for leveraging a Java validated toolkit for the purpose of creating numerous ubiquitous applications for 3D diagnostic radiology. Just as in the desktop application space, leveraging such a toolkit minimizes the scope of ubiquitous application validation. Today, the most standard execution environment in an electronic device is an Internet browser; therefore, a ubiquitous application is web application. This thesis examines an approach where ubiquitous applications can be built using a viewport construct provided by a client-side ubiquitous toolkit that hides the client-server communication between the ubiquitous toolkit and the validated visualization toolkit. Supporting this communication is a Java RESTful web service wrapper around the validated visualization toolkit that essentially “webifies” the validated toolkit. Overall, this ubiquitous viewport is easily included in a ubiquitous application and supports remote visualization and manipulation of volumes on the widest range of electronic devices. Overall, this thesis provided a flexible and scalable approach to developing ubiquitous applications that leverage an existing validated toolkit that utilizes industry standard technologies, patterns, and best practices. This approach is significant because it supports easy ubiquitous application development and minimizes the scope of application validation, and allows medical professionals easy anytime and anywhere access to diagnostic images

ReLaX-VQA:Residual Fragment and Layer Stack Extraction for Enhancing Video Quality Assessment

With the rapid growth of User-Generated Content (UGC) exchanged between users and sharing platforms, the need for video quality assessment in the wild has emerged. UGC is mostly acquired using consumer devices and undergoes multiple rounds of compression or transcoding before reaching the end user. Therefore, traditional quality metrics that require the original content as a reference cannot be used. In this paper, we propose ReLaX-VQA, a novel No-Reference Video Quality Assessment (NR-VQA) model that aims to address the challenges of evaluating the diversity of video content and the assessment of its quality without reference videos. ReLaX-VQA uses fragments of residual frames and optical flow, along with different expressions of spatial features of the sampled frames, to enhance motion and spatial perception. Furthermore, the model enhances abstraction by employing layer-stacking techniques in deep neural network features (from Residual Networks and Vision Transformers). Extensive testing on four UGC datasets confirms that ReLaX-VQA outperforms existing NR-VQA methods with an average SRCC value of 0.8658 and PLCC value of 0.8872. We will open source the code and trained models to facilitate further research and applications of NR-VQA: https://github.com/xinyiW915/ReLaX-VQA

ReLaX-VQA:Residual Fragment and Layer Stack Extraction for Enhancing Video Quality Assessment

With the rapid growth of User-Generated Content (UGC) exchanged between users and sharing platforms, the need for video quality assessment in the wild has emerged. UGC is mostly acquired using consumer devices and undergoes multiple rounds of compression or transcoding before reaching the end user. Therefore, traditional quality metrics that require the original content as a reference cannot be used. In this paper, we propose ReLaX-VQA, a novel No-Reference Video Quality Assessment (NR-VQA) model that aims to address the challenges of evaluating the diversity of video content and the assessment of its quality without reference videos. ReLaX-VQA uses fragments of residual frames and optical flow, along with different expressions of spatial features of the sampled frames, to enhance motion and spatial perception. Furthermore, the model enhances abstraction by employing layer-stacking techniques in deep neural network features (from Residual Networks and Vision Transformers). Extensive testing on four UGC datasets confirms that ReLaX-VQA outperforms existing NR-VQA methods with an average SRCC value of 0.8658 and PLCC value of 0.8872. We will open source the code and trained models to facilitate further research and applications of NR-VQA: https://github.com/xinyiW915/ReLaX-VQA

Digital Image Watermarking Algorithm Using the Intermediate Frequency

Digital image watermarking is one of the proposed solutions for copyright protection of multimedia data. This technique is better than Digital Signatures and other methods because it does not increase overhead. Watermarking adds the additional requirement of robustness. To improve the robustness of digital image watermarking method based on the image frequency, this paper adopts the intermediate frequency to embed the watermarking and proposes an digital image watermarking algorithm based on robust principal component analysis (RPCA) and discrete cosine transform (DCT). Firstly, the high frequency part and the low frequency part of the image are extracted by the RPCA algorithm. Because the high frequency part has complex statistical characteristics, this paper processes the high frequency part with "8×8" DCT method to obtain intermediate frequency coefficients and then the watermarking information is embedded into the obtained intermediate frequency coefficients. The experimental results show that the proposed algorithm leads to satisfactory robustness to the attacks of impulse noise and cropping