A multi-branch hierarchical framework for counterfeit image manipulation localization using noise and frequency features

From Detection to Localization: A Unified Forensics Framework for Fully Synthetic and Tampered Images

The rapid advancement of generative models has significantly worsened the problem of manipulated image detection, as these methods are capable of producing highly realistic forgeries, reinforcing the importance of multimedia forensics. Conventional approaches typically frame image manipulation detection as a binary classification task (real vs. generated), which limits the capability to distinguish and localize different forms of manipulation. To address these constraints, this work extends an existing detector by introducing a unified multiclass framework (real vs. fully generated vs. tampered). In addition to classifying image authenticity, the framework incorporates a segmentation branch to enable pixel-level localization of tampered regions. The proposed approach outperforms selected recent benchmarks, offering an efficient solution with improved classification accuracy and higher IoU scores for the localization task. Find the code at https://github.com/anngal01/From-Detection-to-Localization-A-Unified-Forensics-Framework-for-Fully-Synthetic-and-Tampered-Images.

DEEPFAKE FORENSIC SUITE- IDENTITY MAPPING & MEDIA INTEGRITY VERIFICATION SYSTEM

In the contemporary digital landscape, the exponential proliferation of high-dimensional multimedia data across social platforms, communication networks, and biometric authentication channels is accompanied by an escalating threat of sophisticated generative deception. Deepfakes and synthetic media manipulations present critical systemic risks, ranging from targeted identity fraud to widespread misinformation campaigns. Traditional forensic methodologies—such as pixel-level error level analysis, lighting inconsistency checks, and static rule-based verification—fail to scale efficiently against modern deep synthesis techniques due to heavy compression assumptions, manual feature-engineering constraints, and computational latency. To address these challenges, this monograph presents the design and deployment of the Deepfake Forensic Suite, an automated Identity Mapping and Media Integrity Verification System. The proposed framework establishes a multi-layered security pipeline. First, it implements a high-precision biometric mapping and alignment phase utilizing Multi-task Cascaded Convolutional Networks (MTCNN) to isolate facial regions and eliminate environmental noise. Second, it leverages an optimized MobileNetV2 architecture to extract deep spatial features and compress complex visual attributes into a compact latent representation. By learning the structural characteristics of authentic human faces, the system computes principled prediction probability scores that naturally diverge when processing synthetic manipulations. Furthermore, a statistically robust tri-state classification strategy (Real, Fake, or Uncertain) is established based on validation-set confidence percentiles, enhancing forensic reliability by flagging borderline cases for manual administrative review. The performance of the system is evaluated against established baselines, including traditional Viola-Jones frameworks and shallow convolutional structures. Finally, the practical deployment-readiness of the system is demonstrated through model serialization, a real-time webcam inference API, and a reproducible, interactive web dashboard engineered entirely within the Streamlit framework. The resulting suite provides a lightweight, high-assurance digital forensics solution capable of edge-device execution without requiring slow, cloud-dependent infrastructure.

A Multi-Branch Transformer Based CrossAttention Framework for Computer-GeneratedImage Detection

The rapid development in deep learning-based generative softwares and image rendering tools has led togeneration of massive photorealistic digital content – Fake images, fake videos, fake speech, etc. Such fake digitalmedia may result in communication of misinformation, forgery of digital data, and losing trustworthiness in theinformation source. This poses a significant challenge to the field of digital forensics’ techniques, to which our presentwork attempts to make a contribution, by addressing the problem of differentiating AI-generated images from realphotographs, using transfer learning and multi-branch fusion model. We propose a multi-branch model that integratestwo pre-trained Vision Transformer models (DINO (self-distillation with no labels) and Contrastive Language – ImagePretraining (CLIP)) to extract complementary global features, along with a forensic and a hand-crafted feature branch,which extract low-level discriminating cues. These features are complimentary to each other and hence contribute inimproving the robustness and performance of the model. These features from the four branches are adaptively weightedand combined by a cross-attention module, to give a fused and rich embedding. The model is further optimized by usingaugmentation-invariant loss, center loss and supervised contrastive loss in addition to the cross-entropy loss function.This framework achieves improved accuracy of 95.83% on PRCG dataset, 96.79% on CIFAKE dataset and 99.15% onGenImage dataset as compared to baselines. It also achieved stable cross-generator performance and enhancedrobustness against real world corruptions like Blur, Noise, Compression, and others. The experimental results show agood separability between the classes, and enhanced performance on publicly available datasets.

A Hierarchical ELA-CNN Framework for Image Forgery Detection and Manipulation Localization

Increasing availability of advanced AI-based image creation and editing tools has significantly increased the prevalence of digital image forgery, creating challenges for media authenticity, digital forensics, and information security. This paper presents an intelligent image forgery detection framework that combines multi-quality Error Level Analysis (ELA) with an optimized Convolutional Neural Network (CNN) and heat map-based manipulation localization. The proposed approach utilizes ELA preprocessing at multiple JPEG quality levels to amplify compression inconsistencies associated with image tampering, this is followed by a lightweight CNN architecture trained using advanced optimization techniques, including warmup and cosine annealing learning-rate scheduling, stochastic gradient descent with momentum, and class imbalance handling. To improve interpretability, a heat map generation module is introduced to localize suspicious image regions and provide visual evidence supporting classification decisions which provides interpretability and explainability. The framework was evaluated using the CASIA 2.0 image forgery dataset containing a total of 12,614 images, 7,491 authentic and 5,123 tampered images. Experimental results demonstrate an overall classification accuracy of 94%, outperforming a baseline ELA-CNN implementation while maintaining computational efficiency. The proposed localization mechanism enhances explainability by highlighting potential manipulation regions. A web-based interface and RESTful API were developed to support practical deployment and integration into digital forensic workflows. The results indicate that the proposed framework provides an effective and interpretable smart solution for automated image forgery detection and localization.

ForgerySpotter: Pinpointing Tampered Regions with Multi-scale Evidence and Confidence-Guided Refinement

Image manipulation detection (IMD) is crucial for maintaining the integrity of digital media, as forged images can be used to spread false information and erode public trust. IMD faces two persistent challenges: i) limited generalization to diverse real-world post-processing operations, and ii) imprecise localization accuracy yielding coarse or incomplete tampering regions. Moreover, existing methods often lack interpretability due to the absence of reliable confidence estimation. The primary research often prioritizes feature or architectural improvements while neglecting the integration of detection reliability with localization refinement. In this paper, we propose a unified framework that incorporates multi-dimensional feature extraction, multi-scale feature fusion, and a confidence-guided refine mechanism. Our method captures tampering traces across types and scales adaptively, while the confidence-guided mechanism refines localization maps and estimates pixel-wise reliability. Extensive experiments on multiple datasets demonstrate that the proposed approach achieves state-of-the-art performance and shows strong generalization, validating its effectiveness and practicality.

FAD-Net: a multi-scale image tampering localization network based on high-frequency awareness and dynamic focusing