# Detecting AI-Generated Images Using a Hybrid ResNet-SE Attention Model
## 1 Introduction  
In recent years, significant advancements in artificial intelligence (AI) have enabled the creation of highly realistic digital media, including images, videos, and audio. Generative models such as Generative Adversarial Networks (GANs) and diffusion models have become increasingly capable of producing AI-generated images indistinguishable from real images [1, 2, 3]. While these developments provide opportunities in art, entertainment, and virtual environments [1], they also pose severe threats related to misinformation, identity manipulation, and social trust erosion [3, 4]. The potential misuse of synthetic media has already been observed in numerous cases, including the dissemination of fake news, fraudulent identity creation, and even manipulating political discourse and public opinion [3, 4]. The widespread availability and ease of use of generative AI technologies have raised concerns among researchers and policymakers alike [5]. For instance, recent controversies arose when an AI-generated image won an art prize. Such an unexpected outcome sparked debates regarding authenticity and creative ownership. To summarize, it illustrates broader societal anxieties about the implications of AI-generated content [1]. Therefore, it has become critically important to develop robust methods capable of accurately identifying synthetic images to preserve trust in digital content. Existing approaches for the detection of AI-generated images predominantly rely on traditional deep learning architectures such as ResNet, VGGNet, and DenseNet [6; 7; 8]. These models have demonstrated considerable success across multiple image classification tasks; however, they still encounter difficulties when dealing with subtle, realistic AI-generated images and can be vulnerable to adversarial perturbations [9]. Moreover, standard deep learning methods often lack interpretability and require extensive computational resources, making them challenging to deploy effectively in real-time or resource-constrained environments [10; 11; 12]. Considering these limitations, this research aims to develop an innovative hybrid deep learning framework combining the ResNet architecture with an attention mechanism. The attention mechanism is designed to dynamically identify and focus on regions of the image most indicative of AI-generated artifacts, enhancing the model's accuracy and interpretability [10]. This proposed approach seeks to mitigate the shortcomings associated with traditional deep learning models, enabling more precise and computationally efficient identification of synthetic images. The main objectives of this research are as follows:  
* To evaluate the effectiveness of widely used deep learning models (ResNet, VGGNet, DenseNet) in accurately detecting AI-generated images. * To propose and validate a novel hybrid model integrating ResNet with an attention-based mechanism to achieve superior accuracy and robustness compared with baseline models. * To rigorously assess the performance of the proposed model using the CIFAKE dataset [6], evaluating its potential for real-world applications. The core contribution of this research lies in augmenting the ResNet-50 architecture with Squeeze-and-Excitation (SE) attention blocks to create a hybrid model--SE-ResNet50--designed specifically for AI-generated image detection. These SE blocks dynamically recalibrate feature channels during learning, enabling the network to selectively emphasize subtle generative cues such as unnatural textures, semantic inconsistencies, or repetitive patterns commonly associated with diffusion-based and GAN-generated content. This channel-wise attention mechanism significantly improves the model's ability to distinguish synthetic imagery, resulting in enhanced precision and reduced classification ambiguity, especially in borderline cases.