PMCF: A Progressive Multi-Level Collaborative Framework for Face Forgery Detection

Loupe: image forgery detection and localization based on a multilevel feature collaboration bridge

The popularity of digital image editing techniques has made image forgery detection increasingly important. Existing methods suffer from insufficient robustness and generalization ability, as well as poor synergy between detection and localization tasks. This paper proposes an image forgery detection and localization method based on Multilevel Feature Collaboration Bridge (Loupe). Using Vision Transformer as the backbone to extract multi-level features, we design a Feature Collaboration Bridge Network to realize efficient information interaction between image-level classification and pixel-level segmentation branches via feature concatenation, attention fusion and gating mechanism. The model has only 6.6M parameters, with F1-score of 0.9110 and AUC of 0.9623, outperforming mainstream baseline models. The effectiveness of each component is verified by ablation experiments. Extensive tests under compression and noise interference verify its excellent robustness and generalization, making it suitable for complex real-world scenarios.

Laplacian Pyramid Reweighting With Progressive Residual Learning for Image Forgery Localization

The increasing realism of image manipulations poses significant challenges for forgery localization. However, existing methods are hindered by the limited adaptability of constrained frequency filters and the dilution of subtle forensic cues in deep networks. To address these challenges, we propose the Laplacian pyramid reweighting with progressive Residual Learning framework (LapRL-Net). First, a Laplacian Residual Adaptive Reweighting (LRAR) module is introduced to adaptively modulate multi-scale frequency residuals, enabling flexible extraction of discriminative frequency artifacts. Second, to mitigate feature dilution, we design a Progressive global-local Residual Fusion Module (PRFM) with multi-level residual fusion, which progressively combines global contextual dependencies with local texture details to preserve critical forensic cues. Furthermore, an Edge-Guided Refinement Module (EGRM) is incorporated to enhance boundary accuracy by enforcing geometric consistency via edge supervision. Extensive experiments on multiple benchmarks demonstrate that the proposed method achieves competitive performance in complex forensic scenarios.

Multi-Modal Deep Learning for Image Forgery Detection: A Synergistic Fusion Approach Combining Visual Artifacts and Metadata Consistency Analysis

Multimedia data have been continuously increasing in magnitude, and so has the sophistication of manipulation methods, thereby making the digital forensic investigation process more complicated. The easy access to sophisticated image editing software and AI-generated materials has brought up the issue of information integrity, the reliability of legal evidence, and public trust. Traditional image forensics methods are usually concerned with either the detection of visual artifacts based on convolutional neural networks (CNNs) or based on metadata analysis, frequently independently of each other. This paper presents a multi-modal fusion paradigm, comprising visual feature-based feature extraction and metadata inconsistency-based detectors, to improve the classification strength. A two-stream design is used, comprising a high-level visual artifact capturing the transfer learning-based MobileNetV2 network and an XGBoost classifier that analyses EXIF metadata discrepancies. The heterogeneous representations are merged in a feature-level fusion strategy to generate a final authenticity prediction. It was tested on individual datasets and a compiled dataset of 26,023 images from CoMoFoD, CG-1050 and CASIA v1 and v2. The suggested approach had an overall accuracy of 83.85%, which was higher than the visual-only (68.61%) and metadata-only (75.85%) baselines. These findings show that complementary visual and metadata cues are much more useful in detection, while the use of a lightweight backbone enables efficient, high-throughput forensic analysis suitable for real-world deployment.

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

MAFN-HFL: Multi-Scale Adaptive Fusion Network For Robust Image Forgery Detection Using Hierarchical Feature Learning

The threat of digital image forgery is increasingly becoming a problem to the authenticity of the media, particularly with the introduction of sophisticated editing software and Generative Artificial Intelligence (GAI). To develop a promising forgery detection framework, this research proposes a Multi-Scale Adaptive Fusion Network with Hierarchical Feature Learning (MAFN-HFL), a new Deep Learning (DL) architecture using multi-scale adaptive feature learning and fusion. The dataset consists of images of various domains, natural scenes, portraits, documents, and medical images, and their forgeries. Preprocessing involves noise removal and performing multi-resolution decomposition. Notably, the proposed MAFNHFL incorporates a Multi-Scale Convolutional Attention Network (MSCAN) that extracts both local and global forgery artifacts, an Adaptive Feature Fusion Module (AFFM) that adaptively fuses multi-resolution features according to the manipulation context, and Hierarchical Feature Learning with skip connections to preserve fine-grained visual details. The features of the spatial and frequency domains are captured by a proposed ResNet-50 and Discrete Cosine Transform (DCT) analysis, respectively, through a dual-branch architecture. An Artifact-Aware Attention mechanism achieves a further focus on tampered regions. The hybrid CNNa-Transformer classifier using a confidence-weighted ensemble is capable of providing binary classification as well as localization of manipulated pixels. The proposed model was evaluated on two benchmark datasets using a 70:30 training-test split. Experimental results show strong performance, achieving classification accuracy as high as 99.21% with consistently high precision and low error rates across both datasets. These findings confirm that the proposed MAFN-HFL is a scalable and efficient solution for next-generation image forgery detection.

End-to-end Multi-face Forgery Detection via Progressively-explicit Queries

Deepfake technology has facilitated the spread of malicious content, creating an urgent need for efficient and accurate face forgery detection techniques. Traditional detection methods usually follow a two-stage paradigm, where faces are first detected and then classified as real or fake. However, such approaches can not simultaneously handle multiple faces, and errors in the first stage directly affect the second stage, leading to error accumulation. In this paper, we propose an end-to-end Transformer-based framework, termed Progressively Explicit Query Network (PEQNet), for multi-face forgery detection and localization. Our method extracts instance-aware activation queries from multi-scale, cross-domain features, and jointly processes them with image features through a progressively explicit query-driven decoding network to predict query-dependent masks and classes. During training, we introduce triple contrastive learning to model the mutual exclusivity among real, fake, and background regions. Additionally, we define specific real-or-fake queries to capture global class-level information, which are iteratively refined by the predicted class-aware masks and optimized by aligning them with the ground-truth. Extensive experiments demonstrate that PEQNet achieves superior performance compared with state-of-the-art methods, confirming its effectiveness for multi-face forgery detection in realistic scenarios.