## 2 Related Works  
Our study presents a specialized classification task that focuses on distinguishing AI-generated images from real images. Numerous prior works have addressed this topic using similar or even identical datasets, largely because of the insufficient amount of reliable benchmark datasets that enable consistent performance evaluation and cross-method comparison. As a result, many studies exhibit similarities, with minor methodological variations. The following section gives a thorough random of current developments in AI-generated image identification. Several studies have applied deep learning techniques to tackle this classification problem. A significant portion of existing research relies on transfer learning or various configurations of CNNs. These include both simple multi-layered CNNs and more complex architectures with fine-tuned parameters. Transfer learning remains one of the most frequently adopted methods, with many studies comparing different CNN architectures and reporting their relative performance [38; 39; 41; 42; 43; 44]. For example, one study incorporated activation mapping to highlight the most discriminative features involved in identifying AI-generated images, building on standard CNN architectures [39]. Another work compared Variational Autoencoders (VAEs) with Residual Neural Networks (ResNets), concluding that ResNet significantly outperforms VAEs in this domain [37]. Similarly, a comparative analysis involving pre-trained models such as ResNet, VGGNet, DenseNet, and a custom CNN revealed that DenseNet, enhanced with an additional dense layer, achieved the best results [45]. Additional research examined the performance of various pre-trained models, including VGG16, VGG19, ResNet101, ResNet152, DenseNet121, and DenseNet169, with DenseNet121 performing particularly well when extended with supplementary layers [40]. Other studies have explored novel architectures beyond conventional transfer learning. One such work proposed a dual-input neural network that accepts two versions of the same image, leading to improved classification accuracy [36]. Further comparative studies offer deeper insights. For instance, ResNet50, VGG16, EfficientNetV2B0, and MobileNetV3 Small were evaluated on the CIFAKE dataset, with EfficientNetV2B0 delivering the best performance even without fine-tuning [46]. Another study demonstrated the utility of prototype-based classification using similarity scores, improving training efficiency and inference accuracy [34]. Likewise, comparisons between lightweight CNNs and EfficientNetV2B0 highlighted the latter's superiority [35]. Somestudies also implement attention mechanisms, such as those used in PV-ISM, to further enhance detection performance [47]. There have also been investigations into how image properties affect model performance. For example, one study explored the influence of background elements on detection accuracy, noting potential performance variations depending on visual context [48]. Another method used the local binary pattern (LBP) in addition to fast Fourier transform (FFT) features to make use of texture and frequency-based information, thereby enriching the feature space [49]. Moreover, under the domain of digital forensics, several studies have examined the detection of AI-edited images [48; 50; 51; 52]. While this task differs from identifying fully AI-generated content, it shares significant overlap, as both involve artificial manipulations. Such edits often leverage similar generative techniques like GANs, VAEs, or transformers to add virtual elements rather than synthesizing complete images. Lastly, comparative studies that evaluate models on shared datasets are common, often synthesizing insights across related inquiries [44; 52; 53; 54]. With the exception of more novel approaches--such as FFT-based analysis, multi-input CNNs, and channel-wise classification--most works converge on transfer learning as the most accessible and effective strategy for this classification task.