Aug 2026· Intelligent Data Analysis· 0 citations· 20 references
TL;DR
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.
Abstract
Deepfake technology employs state-of-the-art deep learning to create hyper-realistic synthetic media, which has critical implications for digital authenticity and information security. The spread of such faked content has dire social consequences, facilitating misinformation campaigns, identity theft, and loss of trust in digital media. Today's detection systems experience limitations, such as single-model architectures tending to be unable to generalize the variety of artifact patterns in varying deepfake generation techniques, and most methods are not robust to adversarial attacks. To address these challenges, this study introduces 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. The approach utilizes a region-based preprocessing technique, dividing input faces into Upperface, Lowerface, and Eyes regions using facial landmarks on four varied datasets such as DFDC, Celeb-DF, FF++ and SDFVD to ensure robust generalization. Notably, the inclusion of varied races, ages, and genders in the dataset ensures equitable performance across demographics. Apart from these improvements, most deep learning detection frameworks are still black boxes with limited interpretability and practical applications. This approach integrates Explainable AI (XAI) using Integrated Gradients to interpret decision-making processes, while adversarial testing through input perturbations comprehensively assesses the robustness of the model. Extensive assessment metrics such as accuracy (97%), sensitivity (97%), and specificity (96%) illustrate the system's better performance in balancing deepfake detection effectiveness and generalization across datasets.
DINO-MAC is a simple yet effective model for robust deepfake detection under degraded conditions and is the first-place winner of the “NTIRE 2026 - Robust Deepfake Detection Challenge @ CVPR 2026”, significantly outperforms all competitors.
Chenfan Qu, Lianwen Jin, Junchi Li et al.· 3 citations
A robust, explainable detection framework is presented that combines a CNN backbone for extracting spatial artifacts with an LSTM module for modeling temporal inconsistencies across frames that enhances forensic decision support and increases practical readiness for content verification systems.
Lastone Banda, Esther J.· International Journal of Dat...· 0 citations
As deep learning generation technologies continue to evolve, Deepfake technology has become a major threat to information security, posing significant challenges to tampering detection. Existing detection methods for deep forgeries generally suffer from insufficient generalization capability and interpretability. To address these issues, this paper proposes a Deepfake tampering detection method based on a multimodal large model, termed LLM-FNP. This method integrates frequency-domain noise perception with visual-language understanding. First, the BayarConv module is employed to extract frequency-domain noise features from images, capturing anomalous noise patterns left by the forgery process, and a dual-branch encoder is used to process the original RGB images and frequency-domain feature maps separately. Second, a cross-attention mechanism is applied to achieve cross-modal fusion. Finally, the fused features are processed by a fine-tuned LLaVA large model, which outputs the detection result along with interpretable text. Experimental results on the DFDCP dataset show that the proposed method achieves an accuracy of 78.54% and an AUC of 81.50%. To evaluate the generalization capability of the method, cross-dataset testing on Celeb-DF and WildDeepfake yields accuracies of 73.5% and 71.8%, and AUCs of 76.4% and 75.1%, respectively, all outperforming classical methods such as ResNet+LSTM, further validating its strong generalization ability.
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
Artificial intelligence (AI) is a double-edged sword: while it has achieved remarkable success across a wide range of domains, its deployment also calls for effective oversight and regulation, for which the detection of AI-related content and artifacts is perhaps the most direct and cost-effective approach. To this end, we propose a unified detection framework based on Mahalanobis distance scores (MDS), applicable to several important settings, including the detection of large language model (LLM) generated text, hallucination, watermark, and adversarial examples. A key component of the proposed method is to accurately characterize the positive class--such as human-generated text, factual statements, unwatermarked text, or non-adversarial samples--which requires an efficient and robust estimator of the covariance matrix of deep representations of positive samples before computing the MDS. Since the positive samples typically consist of multiple classes, and these classes may exhibit both homogeneity and heterogeneity, we develop joint estimation methods for both the casewise and cellwise minimum covariance determinant (MCD) estimators. We provide efficient optimization algorithms for both estimators and prove their convergence. We provide a reasonable definition of the breakdown point for the joint estimators and prove their corresponding high breakdown point properties. Empirical evaluations confirm the effectiveness of the proposed detection framework.
Xifeng Zhang, Tao Hu, Yijie Peng et al.· 0 citations
The rapid progress in artificial intelligence (AI), machine learning, and deep learning has led to the development of innovative tools for multimedia content manipulation. While these technologies have legitimate applications in entertainment and education, they have also been misused to create Deep fakes highly realistic fake videos, images, and audio. Deep fakes are often exploited to spread misinformation, propaganda, and political discord, as well as for harassment and blackmail. In response, researchers have developed various detection techniques, including machine learning, spectral analysis, steganography, and feature extraction. This paper presents a systematic literature review of 56 relevant studies published between 2007 and early 2025, analyzing different forgery detection methods. The review highlights the effectiveness of deep learning-based approaches, which excel due to their ability to process large and complex datasets, perform end-to-end learning, adapt to new challenges, and offer robustness, scalability, and continuous improvements. Additionally, widely used benchmark datasets such as Face Forensics++ and Celeb-DF have played a crucial role in advancing research in this domain. Standardized evaluation metrics ensure consistency in model comparisons and facilitate systematic progress tracking. This comprehensive review underscores ongoing efforts to combat image manipulation and safeguard the authenticity of visual media.
MALGWI, YUSUF MUSA, ELKANAH, ADATI CHAHARI, MOHAMMED, ADAU LIMAN et al.· International Journal of Con...· 0 citations
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