Hybrid Semantic and Spectral Ensemble for Robust Synthetic Image Source Attribution

Generalized Synthetic Image Detection with Enhanced RGB-Noise Representation Learning

The rapid advancement of large-scale generative models has accelerated the spread of highly deceptive AI-generated images, making generalized synthetic image detection a critical imperative. Existing forensic networks often struggle with cross-model generalization and realworld degradations due to their reliance on single-domain representations and conventional binary classification optimization. To overcome these limitations, we propose RNSIDNet, a novel forensic framework that achieves robust detection through enhanced RGB-Noise representation learning. Specifically, our method employs a dual-branch architecture where global RGB semantics, extracted by an attention-refined CLIP backbone, dynamically modulate highfrequency noise artifacts captured by Bayar convolutions via a Feature-wise Linear Modulation (FiLM) module. To further enhance the learned representations, we design a Hard Sample-aware Contrastive Learning (HSCL) strategy. By explicitly penalizing challenging training samples, HSCL reshapes the latent feature space to maximize the discriminative margin between pristine and synthetic domains. Extensive experiments across eight public benchmark datasets verify that our model achieves state-of-the-art performance, delivering superior generalization ability, robustness, and computational efficiency. Code and dataset will be publicly available on https://github.com/multimediaFor/RNSIDNet.

Dual-stream framework for real/fake classification and source attribution of AI-generated images using spatial and frequency features

MF2DA: Multi-Level Feature Fusion for Robust Detection and Attribution of Universal AI-Generated Images

The proliferation of hyper-realistic AI-generated images poses significant threats to digital information integrity and forensic accountability. Existing detection methodologies, however, face three critical bottlenecks: vulnerability to real-world distortions such as social media compression, inadequate specialization for socially harmful “ex-regulatory” content, and an inability to perform model attribution essential for effective governance. To address these challenges, we propose the Multi-level Feature Fusion Detection and Attribution Framework (MF2DA), a unified end-to-end pipeline designed for both high-precision detection and reliable model attribution. The core architecture synergizes an Edge Pyramid Fusion ResNet (EPF-ResNet), which captures subtle pixel-level edge artifacts, with a frozen CLIP-ViT to ensure robust semantic generalization. Furthermore, the framework is augmented by the MLLM-Guided Quality Refinement Module (MQRM), which adaptively leverages semantic-agnostic quality features to decouple generative traces from aggressive compression noise. Finally, the Dual-stream Differential Patch Attribution Network (D2PAN) extracts resilient model fingerprints by disentangling micro-textural patterns from semantic interference, thereby achieving precise generator identification. Extensive evaluations on multiple benchmarks demonstrate that MF2DA achieves state-of-the-art performance in detecting both “friendly” and “ex-regulatory” images while maintaining exceptional cross-dataset generalization. By integrating robust detection with precise attribution, this work establishes a practical and accountable forensic solution for the rapidly evolving generative AI landscape.

Radial Residual Frequency: A Semantically Aligned Benchmark and Spectral Detector for AI-Generated Images

A data-generation pipeline that captions real photographs with a vision–language model and regenerates them with modern text-to-image systems, producing semantically aligned real/synthetic pairs that isolate generative artifacts from image content is described.

DTBF: Combining Local Statistical Artifacts and Concept Alignment for Synthetic Image Detection

In general, the cross-generator generalization and robustness against attacks are two main challenges in AI-generated image detection. To address this, we put forward a synthetic image detector (DTBF), a two-branch architecture followed by a jointly-optimized concatenation (JOC), aiming at capturing low-level artifacts and high-level semantics and dynamically fusing them to enhance the generalization and robustness. In the artifact-extracted branch (AE-branch), the local multi-direction binary-encoding patterns (LMBP) are customized to extract and encode the relationships among pixels within each sliding window, resulting in the LMBP distribution serving as a universal fingerprint to distinguish real/fake images. The dual-alignment guided semantic branch (DAS-branch), working as a supplement to AE-branch, designs global context-unrelated prompts and semantic-enhanced prompts to capture global semantic inconsistency and local patch anomalies, strengthening the generalization and robustness of DTBF. Finally, JOC dynamically fuses the two branches through concatenation to amplify the effectiveness of each branch, achieving better generalization and robustness. With the assistance of two complementary branches and JOC, our proposed DTBF significantly outperforms 12 state-of-the-art detectors on two publicly available datasets in terms of detection accuracy and robustness.

Attention-Based Deep Learning Pipeline for AI-Created Image Recognition

The advent of generative AI models that can generate some of the most realistic images including DALL·E, Midjourney, and Stable Diffusion has posed a significant threat to digital forensics and media authenticity verifications. Conventional methods of forgery detection, which focus on splicing or copy-move manipulations, are not very efficient in the case of AI-generated images, which do not contain any explicit tampering traces. This paper presents Attention-Based Deep Learning Pipeline of AI-Created Image Recognition, intended to resolve the increasing challenge of separating AI-synthesized and authentic imagery. The proposed architecture incorporates three integrated branches, including low-level statistical feature extraction, high-level semantic representation learning, and attention-based feature refinement mechanism. The features that are extracted are combined and fed through a fully connected classifier to generate final predictions. The Synthbuster dataset that consists of images of several generative models, including DALL·E 2, DALL·E 3, Firefly, Glide, and versions of Stable Diffusion, were experimentally evaluated. The suggested framework score had an average AUC of 97.4%, as well as high accuracy, precision, recall, and F1-score. These findings support the robustness and generalization ability of the proposed model in detecting AI-generated images in a variety of generators and conditions.