This paper studies how to build deep-fake detectors that both generalize to unseen image generators and provide transparent explanations of their decisions, and proposes a hybrid CLIP-Diffusion architecture that combines a frozen CLIP back-bone with a classifier operating on intermediate features and a diffusion-based reconstruction error head.
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.
DeepFakeBuster is presented as a confidence-calibrated adaptive ensemble for deepfake image detection by fusing together heterogeneous deep learning models built around detecting complementary forensic cues e.g., spatial inconsistencies, boundary artifacts, noise residuals, semantic consistency, and frequency-domain fe...
Rachana Patil, R. Shinde, S. Patil et al.· Scientific Reports· 0 citations
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.
GFRE employs a lightweight autoencoder to model the reconstructability of image representations, producing a reconstruction signal that is inherently generator-agnostic and transferable across diverse generative processes, enabling efficient and scalable deployment.
Qing-Hui He, Hai-Feng Zhang, Bo Liu et al.· Proceedings of the Thirty-Fi...· 0 citations
An AI-based image generation system is introduced that utilizes a Stable Diffusion model fine-tuned with Low-Rank Adaptation (LoRA) for domain-specific image generation, demonstrating that SD+LoRA is an efficient and scalable domain-specific text-to-image generation system.
Ankam Pavitra, R. Mallikharjun, Dr. L Jagadeesh Naik· International Journal of Dru...· 0 citations
A robust, explainable detection framework is presented that combines a CNN backbone for extracting spatial artifacts with an LSTM module for modeling temporal inconsistencies across frames that enhances forensic decision support and increases practical readiness for content verification systems.
Lastone Banda, Esther J.· International Journal of Dat...· 0 citations
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