Jul 2026· 2026 7th International Conference on Smart Systems and Inventive Technology (ICSSIT)· pp. 1064-1070· 0 citations· 23 references
Abstract
Modern machine learning methods such as Generative Adversarial Networks (GANs) are used to automatically produce the fake video content that looks completely real. This threatens the integrity of digital media, journalism, and the public trust. In response to these threats, the DeepFake Reporter Guard offers an explainable AI-based web platform for the journalists to verify the authenticity of a news video. For regional specificity, this system uses XceptionNet model, which was trained on the INDIFACE dataset consisting of Indian faces. The detection methodology involves measuring the facial inconsistencies across the frame level. Once deepfake videos are identified, Grad-CAM-based explainability highlights the manipulated frames in the video and provides visual explanation. The explanation is presented in a way that non-technical users can understand it. The user-friendly web interface allows the journalists or users to upload the videos and get the instant authenticity reports telling whether the video is real or fake with a clear justification. The proposed method is robust and adaptable, making it suitable for real world situations.
Background: Deepfake technology is a major social concern due to the rapid development of Artificial Intelligence (AI), especially in machine learning (ML) and deep learning (DL). Deepfakes are artificially modified videos and images that effectively change a person’s facial features or expressions to misrepresent reality. These videos and images, often unnoticed by casual observers, present significant ethical, political, and social implications, as they can spread misinformation, damage reputations, and influence public perception. This study is an attempt to detect artificially modified videos by analyzing each frame using DL. We use ResNet-50 architecture, a well-known Convolutional Neural Network (CNN) model, to identify tampered videos. Methods: The system is trained using the Celebrity Deep Fake dataset, which includes numerous original and fake video samples. The model assesses whether each frame is original or tampered with after the videos have been split into frames. The system is tested and evaluated using standard metrics, including accuracy, precision, recall, and F1-score. Results: The model achieved 82.33% accuracy, 76.70% precision, 89.15% recall, and an F1-score of 82.46%. These results indicate that deepfake videos were correctly detected and that the model was efficient at identifying most real deepfake instances. In addition, the F1-score of 82.46% is further evidence of the model’s stability, as it ensures both high accuracy and coherence across numerous cases. Conclusions: The findings indicate that the proposed model works effectively compared to existing deepfake methods. In the future, we intend to use a richer dataset with more resources, which may further enhance the accuracy of the model.
Iftikhar Alam, Malik Ahsan Kamran, Ehaab Ullah· The International Journal of...· 0 citations
The evolution of sophisticated generative artificial intelligence has led to the rapid development of very realistic manipulated images and videos, posing substantial risks for digital trust, cyber security, and multimedia authenticity. Advanced Deepfake generation technologies result in the creation of believable forgery media which become hard to differentiate from authentic media; this leads to misinformation, identity spoofing, and digital scams. Therefore, precise and effective authentication of multimedia becomes an imperative requirement for digital forensics investigation and online content authentication. This research paper presents a ResNet-powered deep feature learning approach for detecting Deepfake images and videos. The suggested approach normalizes and resizes images, while videos are decomposed into frames for thorough spatial-temporal analysis. The hybrid convolutional neural network model, which is built on top of ResNet architecture, extracts discriminative features that represent subtle manipulation traces, face texture inconsistency, and structural abnormalities. In addition, inverted residual blocks and linear bottlenecks are used to increase computational efficiency. The deep learning-based feature extraction process is then followed by the classification stage to distinguish between genuine multimedia content and forged multimedia content. From experimental studies, it can be shown that the proposed framework helps to enhance the detection rate, robustness toward new Deepfake methods, and enables real-time implementation. The research provides an effective solution for multimedia authentication applications.
Bella Inba Suganthi V, S. Jose· International Journal of Sci...· 0 citations
A novel method for video deepfake detection that assimilates the Pelican Optimization algorithm with a DL model jointly named as Pelican Attention Stacked Bidirectional Long-Short Term Memory (PAttSBiL), aimed at improving recognition accuracy and efficacy is presented.
D. R. Agrawal, Farha Haneef· Multimedia tools and applica...· 0 citations
This review presents a comprehensive analysis of recent deep learning and transfer learning techniques for fake image detection, examining widely adopted convolutional neural network architectures, benchmark datasets, evaluation metrics, and current research developments.
Nisha Parveen, Anjali Saxena· International Journal for Re...· 0 citations
In this paper, a general overview of the use of deep learning in the battle against the booming deepfake industry problem is discussed. Uses of deep learning include, but are not limited to, natural language processing, machine learning, and computer vision and are responsible for a multitude of novel applications. Meanwhile, the growing number of convincing videos and images has also been worrying. This technology can have serious implications online when used for nefarious purposes, such as fake news stories, persona impersonation, financial scams, and distribution of unwanted explicit visual material. Those such as celebrities and politicians are particularly vulnerable to this. The current work evaluates the performance of four deep learning models, namely InceptionResNetV2, VGG19, a standard CNN and Xception, in the fields of deepfakes generation and identification. The evaluation carried out using a Kaggle dataset of deepfakes confirms that Xception outperforms the other models analysed in detecting deepfakes. In a rapidly evolving landscape, where malicious deepfakes are becoming more common every day, it is necessary to develop dependable detection systems to reduce the impact they can have on society.
Rama Rao Adimalla· International Journal of Com...· 0 citations
An ensemble-based approach, namely the X-Iv2 Ensemble approach, merging Inception ResNet v2 and Xception Net based on their complementary architectures to enhance feature extraction and classification is introduced, integrating Explainable AI (XAI) using Integrated Gradients to interpret decision-making processes.
P. P. Sudharsana, R. Rajalaxmi· Intelligent Data Analysis· 0 citations
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