## 1 Introduction  
In the last decade, research regarding artificial intelligence (AI) related to photographic and visual data has garnered increasing attention, primarily due to the proliferation of algorithms for deep learning and their rapidly improving performance. These advancements have enabled a broad spectrum of applications across diverse domains. Notable examples include visual arts [1, 2], health sciences [3, 4, 5, 6, 7, 8], earth sciences [9, 10, 11, 12], criminal investigations [13, 14], engineering [15, 16, 17, 18, 19, 20, 21, 22], and psychology [23, 24]. The reason for these many examples is the widespread use of mobile devices that can take high-quality photographs [25]. In addition, in disciplines where visual data plays a critical role, such as smart cities, space, and health, the fact that observation devices are of increasingly high quality in terms of hardware is motivating for artificial intelligence studies in these fields [26]. In parallel with these developments, recent studies have explored image manipulation techniques for privacy and visual realism. For example, generative models have been used for facial anonymization [27], while advanced filtering methods enhance details and correct illumination in images [28, 29]. On the other hand, image filtering has gained attention for its role in robustness and clarity. Studies have introduced low-pass filters to defend against adversarial attacks [30], analyzed filter types for mesh and texture preservation [31], and proposed hybrid filtering methods for noise reduction and structural preservation [32; 33]. Another area of research in this context focuses on synthetic media content. Current developments in generative artificial intelligence have significantly reshaped image-producing technologies, empowering computational models to produce highly realistic visuals that often closely resemble or are virtually indistinguishable from genuine imagery. While this progress brings innovation across fields such as art, entertainment, healthcare, and surveillance, it simultaneously poses significant challenges in the detection and authentication of synthetic media. Recent studies have tackled this issue from various angles: [PERSON] et al. introduced a robust multimodal prototype-based approach (PUDD) to deepfake detection [34]; [PERSON] and [PERSON] quantified how differently real and artificial images behave in vision models [35]; [PERSON] and [PERSON] proposed a dual-input neural architecture to integrate structural and contextual cues [36]; and [PERSON] and [PERSON] performed an comparative evaluation of deep learning-based methods for image authentication [37]. [PERSON] et al. explored the classification of various AI-generated content using machine learning and knowledge graphs [38]. Other contributions, such as CIFAKE by [PERSON] and [PERSON], emphasize interpretability in the classification of fake content [39], while [PERSON] et al. leveraged transfer learning for efficient adaptation to novel generative techniques [40]. Together, these approaches reflect a growing consensus in the research community. Identifying artificial intelligence-generated photos is not merely a technical task, but a critical safeguard against misinformation, digital manipulation, and loss of trust in visual images. As generative models become more sophisticated, the urgency to develop reliable, explainable, and adaptive detection methods continues to intensify. Traditionally, convolutional neural networks (CNNs) and transfer learning approaches have been the preferred methodologies for such detection tasks. Models like ResNet, pre-trained on massive datasets, have offered high performance across various classification problems. However, these architectures carry inherent limitations, particularly in their potential to model long-term reliance and the global environment within image data. Transformers, with their self-attention mechanisms, address these shortcomings elegantly.