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Open research questions in Digital Media Forensic Detection

50 unresolved questions extracted from the limitations and future-work sections of 185 Digital Media Forensic Detection papers in our library. Each links back to the study that raised it.

What the literature leaves open

  • Poor generalisation of deepfake detection tools to unseen data distributions - Lack of confidence intervals and error documentation - Limited explainability of detection results - High false positive rates in realistic deployment conditions

    Beyond benchmark accuracy: Evaluating deepfake detection tools for digital forensic admissibility through a systematic review · 2026 · DOI

  • Reliably identifying manipulated media remains challenging due to increasing content realism and the diversity of deployment contexts. Developing deepfake detection models that can generalise across different accents and formats is a challenge.

    Irish-Accented English Audio-Visual Deepfake Datasets with Deep Packet Inspection-Inspired Media Integrity Validation · 2026 · DOI

  • Future research can focus on improving the scalability and computational resources of the proposed framework. Future research can explore the application of the proposed framework in other domains, such as healthcare and finance. Future research can investigate the use of other explainable AI techniques and graph neural networks for deepfake attribution and cross-platform disinformation campaign tracking.

    A Hybrid Explainable Artificial Intelligence Framework for Deepfake Attribution and Cross-Platform Disinformation Campaign Tracking · 2026 · DOI

  • Existing deep learning-based deepfake detection systems have limitations, including binary detection and lack of explainability. Current XAI research concentrates on deepfake detection and provides limited support for attribution decisions. The lack of integration with cyber threat intelligence systems is a significant research gap.

    A Hybrid Explainable Artificial Intelligence Framework for Deepfake Attribution and Cross-Platform Disinformation Campaign Tracking · 2026 · DOI

  • We examine evaluation benchmarks and protocols, highlighting cross-generator generalization as the field’s central open challenge.

    Deepfakes and Synthetic Media: Generation, Detection, and Governance · 2026 · DOI

  • In particular, the effectiveness of image-level detectors in the video domain has not been systematically assessed.

    FakeI2V-Bench: Benchmarking the Applicability of Image-level Deepfake Detectors for Deepfake Video Detection · 2026

  • Pretrained self-supervised representations have emerged as a core component of current deepfake detection methods, yet it remains unclear which of their properties make real and fake media distinguishable.

    Foundation Models are Implicit Deepfake Detectors · 2026

  • Conventional detection approaches are often ineffective against sophisticated manipulations. There is a need for a reliable system for detecting DeepFake voice and video content.

    An Intelligent Deep Learning FrameWork for DeepFake Voice and Video Detection · 2026 · DOI

  • The need for a comprehensive hybrid forensic detection system, - the limitation of existing methods in terms of computational cost and generalisability.

    Copy-Move Image Forgery Detection Using Hybrid DyWT- SIFT-G2NN with Agglomerative Clustering · 2026 · DOI

  • Generalization across datasets remains a major challenge. Neural networks tend to deteriorate when tested on new data with different distributions. Computational complexity is a limitation for transformer-based architectures.

    DeepForgeryNet: a hybrid CNN–LSTM and transfer learning framework for robust image forgery and deepfake detection · 2026 · DOI

  • Investigating the application of the proposed framework to other domains. Exploring the use of other machine learning techniques for image forgery and deepfake detection.

    DeepForgeryNet: a hybrid CNN–LSTM and transfer learning framework for robust image forgery and deepfake detection · 2026 · DOI

  • The creation of photorealistic fake visual media poses significant challenges for digital security. Existing techniques for detecting deepfake images have limitations and may not be effective in all cases.

    An Efficient Deep Learning Framework for Photorealistic Fake Visual Media Detection · 2026 · DOI

  • The development of a universal method for detecting alteration in digital documents. The integration of different detection techniques to enhance the overall effectiveness of forensic tools.

    Evolving Paradigms in Digital Document Forgery Detection: From Heuristics to Multimodal · 2026 · DOI

  • The lack of a universal method for detecting alteration in digital documents. The need for more research into the analysis of alterations in offline scanned documents.

    Evolving Paradigms in Digital Document Forgery Detection: From Heuristics to Multimodal · 2026 · DOI

  • The scope of this study is limited to the forensic examination and comparison of real images and AI-generated deepfake images using digital forensic analysis techniques.

    AI-BASED DETECTION AND FORENSIC ANALYSIS OF DEEPFAKE IMAGES · 2026 · DOI

  • Researchers may analyze video deepfakes in addition to image-based deepfakes. Digital forensic laboratories should adopt modern forensic tools for image authentication. Further research may focus on real-time deepfake detection systems for social media platforms.

    AI-BASED DETECTION AND FORENSIC ANALYSIS OF DEEPFAKE IMAGES · 2026 · DOI

  • The lack of a widely validated or forensically admissible method for detecting deepfakes - The gap between academic benchmark performance and forensic operational requirements

    Beyond benchmark accuracy: Evaluating deepfake detection tools for digital forensic admissibility through a systematic review · 2026 · DOI

  • Most existing image forgery detection systems are computationally intensive and not suitable for real-time deployment on low-power devices like Raspberry Pi. Many methods only detect forgery at the image level without accurately localizing the tampered regions.

    AI Powered Fake Image Detection System using ResNet and U-Net · 2026 · DOI

  • Existing video forensic methods predominantly operate on short, independent clips, and thus fail to capture realistic scenarios where AI-generated content is sparsely embedded within otherwise authentic footage.

    Explainable Forensics of Manipulated Segments in Untrimmed Long Videos · 2026

  • The lack of effective methods to detect copy-move forgery in digital images. The need for a hybrid multi-scale approach to detect copy-move forgery.

    A Robust Hybrid Multi-Scale Approach to Detect Copy-Move Forgery in Digital Image · 2026 · DOI

  • To explore the application of the proposed method in various domains such as journalism, social media, legal, and scientific research. To evaluate the performance of the proposed method on different types of images and image editing software. To develop more advanced techniques to detect image tampering and restore trust in visual data.

    Multi-channel Prediction Residue Modeling(MPRM) Using Second Order Residual Statisticsfor Enhanced CFA Artifact Based ForgeryDetection · 2026 · DOI

  • Existing CFAA based splicing detection methods often rely on single channel, exhibit high computational complexity, and show degraded performance under JPEG compression. The ability to effectively capture cross-channel dependencies and maintain robustness under heavy JPEG compression remains limited. There is a need for a reliable and robust technique to authenticate image integrity.

    Multi-channel Prediction Residue Modeling(MPRM) Using Second Order Residual Statisticsfor Enhanced CFA Artifact Based ForgeryDetection · 2026 · DOI

  • The paper does not explicitly identify a research gap. The original article discussed the benefits of blended learning in enhancing the training process of ML models for image classification.

    Retraction Notice: Blended Learning for Machine Learning-based Image Classification · 2026 · DOI

  • Extending the framework to other modalities, such as text and audio. Improving the robustness of the approach to extreme pose variations and heavy occlusion.

    Deepcheck: A Unified Multimodal Deepfake Detection Framework with Cross-Modal Consistency Analysis, Learned Fusion, and Explainable AI · 2026 · DOI

  • Existing deepfake detectors have limitations, such as lack of interpretability and inability to detect cross-modal inconsistencies. Single-modality detectors are not effective in real-world scenarios.

    Deepcheck: A Unified Multimodal Deepfake Detection Framework with Cross-Modal Consistency Analysis, Learned Fusion, and Explainable AI · 2026 · DOI

Most-cited papers in Digital Media Forensic Detection

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50 open questions have been extracted from the limitations and future-work passages of 185 Digital Media Forensic Detection papers in our library. Each one below links back to the study that raised it, so you can read the original claim in context.

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