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With the mushroom growth of state-of-the-art digital image and video manipulations tools, establishing the authenticity of multimedia content has become a challenging issue. Digital image forensics is an increasingly growing research field that symbolises a never ending struggle against forgery and tampering. This survey attempts to cover the blind techniques that have been proposed for exposing forgeries. This work dwells on the detection techniques for three of the most common forgery types, namely copy/move, splicing and retouching.

Social networks are major sources of image sharing and secret messaging among the people. To date, such networks are not strictly bounded by copyright laws due to which image sharing, secret messaging, and its authentication is vulnerable to many risks. In addition to this, maintaining the confidentiality, integrity, and authenticity of secret messages is an open challenge of today’s communication systems. Steganography is one of the solutions to tackle these problems. This paper proposes a secure crystographic framework for authenticity of visual contents using image steganography, utilizing color model transformation, three-level encryption algorithm (TLEA), and Morton scanning (MS)-directed least significant bit (LSB) substitution. The method uses I-plane of the input image in HSI for secret data embedding using MS-directed LSB substitution method. Furthermore, the secret data is encrypted using TLEA prior to embedding, adding an additional level of security for secure authentication. The qualitative and quantitative results verify the better performance of the proposed scheme and provide one of the best mechanisms for authenticity of visual contents in social networks.

Deepfakes and adjacent synthetic-media capabilities have become a systemic challenge for information integrity, security, and digital trust. Countermeasures now span passive detection methods that infer manipulation from content traces, active provenance systems that cryptographically bind metadata to media, and watermarking approaches that embed detectable signals into content or generative processes. This review presents a rigorous synthesis of tools and technologies to combat deepfakes across modalities (image, video, audio, and selected multimodal settings), drawing primarily from the peer-reviewed literature, standardized benchmarks, and official technical specifications and reports. The review analyzes detection methods, provenance and authentication technologies, with emphasis on cryptographic manifests and threat models, watermarking and content provenance, including diffusion-era watermarking and industrial deployments, adversarial robustness and attacker adaptation, datasets and benchmarks, evaluation metrics across tasks, and deployment and scalability constraints. A dedicated section addresses legal, ethical, and policy issues, focusing on emerging transparency obligations and platform governance. The review finds that no single countermeasure is sufficient in realistic adversarial settings. The strongest practical approach is a layered defense that combines provenance, watermarking, content-based detection, and human oversight. The study concludes with limitations of the current evidence base and prioritized research directions to improve generalization, interoperability, and trustworthy user experiences.

This research focuses on the detection of deepfake images and videos for forensic analysis using deep learning techniques. It highlights the importance of preserving privacy and authenticity in digital media. The background of the study emphasizes the growing threat of deepfakes, which pose significant challenges in various domains, including social media, politics, and entertainment. Current methodologies primarily rely on visual features that are specific to the dataset and fail to generalize well across varying manipulation techniques. However, these techniques focus on either spatial or temporal features individually and lack robustness in handling complex deepfake artifacts that involve fused facial regions such as eyes, nose, and mouth. Key approaches include the use of CNNs, RNNs, and hybrid models like CNN-LSTM, CNN-GRU, and temporal convolutional networks (TCNs) to capture both spatial and temporal features during the detection of deepfake videos and images. The research incorporates data augmentation with GANs to enhance model performance and proposes an innovative fusion of artifact inspection and facial landmark detection for improved accuracy. The experimental results show near-perfect detection accuracy across diverse datasets, demonstrating the effectiveness of these models. However, challenges remain, such as the difficulty of detecting deepfakes in compressed video formats, the need for handling noise and addressing dataset imbalances. The research presents an enhanced hybrid model that improves detection accuracy while maintaining performance across various datasets. Future work includes improving model generalization to detect emerging deepfake techniques better. The experimental results reveal a near-perfect accuracy of over 99% across different architectures, highlighting their effectiveness in forensic investigations.

In a global society, learners must communicate effectively across diverse cultural contexts, but traditional classroom learning can fall short of preparing students for real‐life demands. Simulations can shift students' focus from learning about language to using language for authentic tasks. This study developed and evaluated a digital, educational language simulation called a Playable Case Study (PCS). Students investigated questions about the Spanish Civil War as curators in a fictional museum and produced an exhibit for a real public audience. Researchers examined the question: How do the design features of a historically‐contextualized, project‐based PCS simulation influence student engagement in university Spanish language courses? Findings based on classroom observations, pre‐/post‐surveys, and instructor interviews (a) offer insights into four types of authenticity that affect student engagement (linguistic, cultural, disciplinary, and technological), (b) show that constructivist, multimedia curation tasks can stimulate autonomous learning, and (c) reveal technological authenticity's influence on student engagement.

This paper presents a low complexity configurable semi-fragile watermarking scheme for content-based H.264/AVC authentication, which allows content-preserving manipulations such as video transcoding techniques, while it is very sensitive to content-changing and frame manipulations. A low cost spatial analysis is exploited to maximize robustness and lessen the incurred distortion. Invariant features are extracted from intra macroblocks, and frame index timing information constituting the content-based authentication codes are embedded/extracted into Quantized DCT (QDCT) coefficients in a Group of Pictures GOP-based fashion. The embedded watermark can be detected and verified by partially decoding intra and inter macroblocks without the prior knowledge of the original video or complete video decoding. Spatial and temporal tampering can be characterized and localized by comparing the extracted and re-generated feature-based watermarks. Experimental results demonstrate that the performance of the proposed scheme is indeed superb in terms of bit-rate and perceptual quality. Moreover, various types of attacks have been simulated and characterized.

H.264/AVC-based products have grown tremendously in social networks; issues of content-based authentication become increasingly important. This paper presents a blind fragile watermarking scheme for content-based H.264/AVC authentication, which enjoys high sensitivity to typical video attacks. A spatiotemporal analysis is exploited to guarantee a minimum impact on perceptual quality and bit-rate increment. The watermark features are extracted from intra/inter prediction modes of intra/inter macroblocks, constituting the content-based Message Authentication Code (MAC) which is embedded/extracted in a Group-of-Pictures GOP-based fashion utilizing the syntactic elements of the Network Application Layer (NAL) units from the compressed bitstream. It’s unnecessary to fully decode a compressed bitstream before the embedding or detection processes. A content-based key is generated to control fragile watermark generation, embedding, extraction, and verification algorithms. Additionally, fragility is ensured by selecting the last nonzero quantized ac residuals for watermark embedding. The embedded watermark can be detected and verified by means of partially decoding intra/inter prediction modes from syntactic elements of the bitstream without the prior knowledge of the original video or complete decoding. Experiment results demonstrate that the performance of the proposed scheme is excellent in terms of bit-rate and perceptual quality. Furthermore, various types of content-preserving and/or content-changing attacks can be detected efficiently.

Background: Undergraduate speech-language therapy students often find it difficult to see the relevance of theoretical module content, which may negatively influence their motivation to learn. The real world of their future profession can be brought to life in the theory classroom by including authentic case study examples. Video case studies are well suited to illustrating communication disorders and may also be easier to remember and relate to information already in the long-term memory. Objectives: This article describes the perceptions of undergraduate students regarding the inclusion of authentic video cases in a theoretical module on developmental communication disorders. Methods: A qualitative, interpretivist research design was followed. Focus-group interviews were conducted with 22 second-year students in the programme B Speech-Language and Hearing Therapy. A modified contextualised content analysis approach was used to analyse interview data. Results: The use of authentic video cases was perceived positively by participants. Seeing a realistic example of a person with communication difficulties made it easier to understand, remember and engage with the module content. Participants also felt they could more easily imagine themselves in that clinical context, which seemed to (re-) awaken in them a sense of purpose and motivation. Being presented with real-life communication problems made them realise the relevance of their profession. However, participants experienced cognitive overload at times when the processing requirements of a task exceeded their available cognitive capacity. Conclusion: Video cases are valuable tools to enhance students’ engagement with theoretical content. To avoid cognitive overload, a scaffolded multimedia learning experience needs to be provided.

With today’s global digital environment, the Internet is readily accessible anytime from everywhere, so does the digital image manipulation software; thus, digital data is easy to be tampered without notice. Under this circumstance, integrity verification has become an important issue in the digital world. The aim of this paper is to present an in-depth review and analysis on the methods of detecting image tampering. We introduce the notion of content-based image authentication and the features required to design an effective authentication scheme. We review major algorithms and frequently used security mechanisms found in the open literature. We also analyze and discuss the performance trade-offs and related security issues among existing technologies.

The protection and enforcement of intellectual property rights for digital media has become an important issue in many countries of the world. There is increase in the popularity and accessibility of the Internet to record, edit, replicate and broadcast multimedia content which has necessitated a high demand to protect digital information against illegal uses, manipulations and distributions. Digital watermarking technique which is the process used to embed proprietary information into multimedia digital signal provides a robust solution to this problem. This paper reviews different aspects and techniques of digital watermarking for protecting digital contents. It also explores different application areas of digital watermarking such as copyright protection, broadcast monitoring, integrity protection etc.