Jul 2026· Al-Noor Journal of Engineering Management and Computer Science· 0 citations· 10 references
TL;DR
The proposed Attention-Based Deep Learning Pipeline of AI-Created Image Recognition incorporates three integrated branches, including low-level statistical feature extraction, high-level semantic representation learning, and attention-based feature refinement mechanism, which support the robustness and generalization ability of the proposed model in detecting AI-generated images in a variety of generators and conditions.
Abstract
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.
With the rapid advancement of generative artificial intelligence (AI), the visual fidelity of synthesized images has increased dramatically, posing serious challenges to the verification of digital content authenticity. Existing AI-generated image detection methods often suffer from limited generalization and robustness, particularly when confronting unknown generative models or complex post-processing perturbations. To attenuate such deficiency, we propose an AI-generated image detection scheme. Leveraging a frozen contrastive language–image pre-training with Vision Transformer as visual backbone, the network extracts and stacks multi-scale intermediate features from the transformer modules to effectively capture both low-level and high-level forensic fingerprints. Based on this representation, we introduce an improved convolutional block attention module, which adopts a cascaded design by first applying channel-wise attention and then spatial attention. This design enables the network to adaptively select informative feature hierarchies while strengthening the representation of local generative artifacts. To further optimize the feature space structure, we propose a hard-sample-aware contrastive learning loss. It dynamically mines hard samples to enhance intra-class compactness and inter-class separability. In addition, we construct a mixed-source training image dataset named mixed-source AI-generated image dataset, which covers diverse generative paradigms. Large-scale testing results show that our proposed scheme ranks first on average across seven benchmark datasets, with accuracy 82.96% and the area under receiver operating characteristic curve 93.43%, demonstrating its outstanding generalization ability. Code is publicly available at https://github.com/multimediaFor/MIDNet.
Unknown authors· Journal of Electronic Imagin...· 0 citations
This project presents an explainable deep learning framework for identifying real and AI-generated images using the NASNet architecture and achieves high detection accuracy while providing interpretable visual explanations, making it suitable for digital image verification, media authentication, and cybersecurity applications.
Panduga Mounika, Dr.CH. Buchi Reddy· American Journal of AI Cyber...· 0 citations
This study investigates the automatic classification of real and AI-generated flower images using fine-tuned transfer learning models and shows that Swin Transformer-Tiny achieved the best overall performance, reaching an F1-score of 88.64% and outperforming the other architectures.
Unknown authors· NATURENGS MTU Journal of Eng...· 0 citations
Recent advances in generative models like Style-GAN2 and diffusion models produce highly realistic AI-generated faces, necessitating reliable detection for identity verification and security applications. This study presents a systematic empirical comparison of Vision Transformer ViT-B/16 and EfficientNetV2-B3 via transfer learning for binary classification of real versus AI-generated faces. A combined dataset of 200,000 images was constructed, integrating real faces from CelebA and FFHQ with synthetic faces from StyleGAN2 and modern text-to-image models including Flux, DALL-E 3, and Stable Diffusion XL. Both models were initialized with ImageNet pretrained weights and optimized using a two-stage fine-tuning scheme that progressively unfreezes backbone layers. Performance was evaluated on accuracy, precision, recall, F1-score, ROC-AUC, PR-AUC, and computational efficiency metrics including latency and throughput. We further analyzed per-generator performance, model interpretability via Grad-CAM and Attention Rollout, robustness against image degradations, and cross-dataset generalization. EfficientNetV2-B3 achieved top performance with 0.9986 accuracy and F1-score, slightly outperforming ViT-B/16 (0.9979 accuracy), with both achieving near-perfect ROC-AUC. Both models maintained above 0.99 accuracy under JPEG compression and resizing. However, ViT-B/16 demonstrated superior cross-dataset generalization (0.9474 accuracy) on an external StyleGAN test set, compared to EfficientNetV2-B3 (0.8901). Since EfficientNetV2-B3 uses approximately 6.7 times fewer parameters and achieves 2.6 times higher throughput, it is recommended for in-distribution high-throughput detection, while ViT-B/16 is preferred for cross-generator scenarios.
G. Indra, K. Lhaksmana· International Conference on...· 0 citations
Text-to-image generative models have advanced rapidly, with modern Diffusion Transformer architectures producing images that are increasingly difficult to distinguish from human-created artwork. This development has raised significant concerns regarding copyright protection, misinformation, fraud, impersonation, and the authenticity of digital content. Most AI-art detectors are trained and evaluated on the same generator family, leaving robustness to newer architectures underexplored. In this chapter, we analyze generator shift based on a Stable Diffusion 3.5 Medium (SD3.5m) artwork dataset spanning ten art styles through reverse prompting of held-out human artwork samples. Five detectors are trained on U-Net-based latent diffusion artwork and evaluated in a zero-shot cross-generator setting on the SD3.5m dataset. Deep learning models perform strongly in-distribution but degrade under generator shift, misclassifying many SD3.5m images as human while human false positives remain low. The CLIP ViT-L/14 model performs best overall, while Grad-CAM analysis reveals weaker and more diffuse activation on false negatives. These findings highlight a generalization gap in current AI-art detectors and motivate the development of detectors as one component of a layered defense that remains reliable across rapidly evolving generative architectures.
Text-to-image generation is an increasingly fast-paced field of generative artificial intelligence, consisting of synthesizing images of high quality and semantic consistency based on natural language descriptions. In this paper, we give an extensive overview of the approach to text-to-image generation using deep learning, including the most common core model families, architecture designs, training approaches, and evaluation systems. We discuss the paradigms of the generative adversarial networks (GANs), variational autoencoders (VAEs), transformer-based designs, and diffusion models, with the last one representing the state of the art in image generation models. The review also discusses key aspects of pipelines such as text encoding, cross-modal alignment, mechanisms of attention, and decoding images. Popular datasets, methods, and metrics of evaluation, including Fréchet Inception Distance (FID) and CLIP-based similarity, are discussed. The application domains that involve creative content creation, medical imaging, education and industrial design are critically discussed. Despite significant advances, various issues still exist, such as low stability in training, excessive computational complexity, amplification of bias, generated images, and text–image alignment errors. Moral and social issues, such as misinformation, intellectual property, and equity, are critically examined. Lastly, we present future research directions to more controllable, more efficient and more interpretable text-to-image systems, focusing on multimodal foundation models and human–AI collaborative design.
