Synthetic Image Detection Across Different Generative Models

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.

Generated Images Are Easier to Forget: A Machine Unlearning Perspective for Synthetic Image Detection

This work establishes a new paradigm for generated image detection by recasting the detection task as a problem of machine unlearning, and introduces two detection methods: data-free detection, which prunes model parameters to induce unlearning without data access, and data-driven detection, which optimizes LVMs to unlearn knowledge tied to generated images.

Robustness of Synthetic Image Detec-tors in the Wild: Cross-Domain Evaluation and Visual Literacy-Based Error Analysis

Effective Synthetic Image Detection via Noise Residual Clustering

The rapid advancement of generative artificial intelligence (AI) has made synthetic images remarkably realistic, posing security threats such as misinformation and fraud. It is significant to detect the synthetic image in the manner of passive and blind image authentication. Most existing detectors rely on supervised training with large labeled datasets, leading to high costs and degraded performance on unknown generative models. To attenuate such deficiencies, we propose a training-free detection method. Specifically, noise residual fingerprints are first extracted by a simple yet effective pre-trained Noiseprint++ model. Then multi-scale features are further extracted from such residual by a frozen Vision Transformer (ViT), followed by adaptive weighted fusion. Only a few real image samples are used needed to initialize the clustering centers for unsupervised K-Means, distinguishing real and synthetic images without training. Extensive evaluations on four benchmark datasets show that our proposed scheme achieves an average accuracy of 82.2%, outperforming the state-of-the-art detectors on generalization ability. Superior performance is gained on the popular diffusion type of synthetic images, and the effectiveness of each module is validated by ablation studies. Source code will be publicly available at https://github.com/multimediaFor/NoiseCluSID.

Understanding Why Foundation Models Work for Diffusion-Generated Image Detection

This work investigates what cues are exploited by foundation-model-based detectors to distinguish real images from diffusion-generated ones and suggests that foundation-model-based detectors succeed by capturing non-semantic low-to-mid frequency distributional discrepancies between real and diffusion-generated images.

Deepfake Image Detection Using Machine Learning

The rapid advancement of generative artificial intelligence has significantly increased the creation of highly realistic manipulated images, commonly known as deepfakes. These synthetic images pose serious threats to digital security, privacy, and information authenticity, as they are often indistinguishable from real images through human observation. This growing challenge necessitates the development of automated and reliable detection systems capable of identifying subtle visual inconsistencies in manipulated content.This paper presents a deep learning–based approach for detecting deepfake images using a transfer learning framework. A pretrained convolutional neural network model, MobileNetV2/EfficientNet, is utilized for feature extraction, followed by classification layers to distinguish between real and fake images. The proposed system incorporates image preprocessing techniques such as resizing, normalization, and data augmentation to improve model robustness. The model is trained on benchmark datasets containing both genuine and manipulated images and evaluated using performance metrics such as accuracy, precision, recall, and F1-score. Experimental results demonstrate that the proposed approach effectively identifies deepfake images with high accuracy, making it suitable for applications in digital forensics, media verification, and cybersecurity.