## 2 Related Work  
The rapid advancements in deep learning have catalyzed the development of sophisticated techniques for generating synthetic media, which are broadly known as deepfakes. These manipulations encompass not only face swaps or reenactments but can also extend to audio and textual modalities, posing multifaceted challenges for content authenticity. While they enable creative and legitimate applications, such as educational tools and cinematic effects, deepfakes also introduce unprecedented threats to the reliability of digital information. Deepfake generation methods primarily leverage generative adversarial networks (GANs) and autoencoders. GANs, introduced by [PERSON] et al. [1], consist of two neural networks--a generator and a discriminator--that engage in a competitive process. The generator creates synthetic content, while the discriminator attempts to distinguish between real and fake samples. This adversarial training paradigm has proven highly effective, giving rise to a variety of face-swapping and reenactment systems [11], culminating in the mass adoption of tools such as FaceSwap and DeepFaceLab. [PERSON] and [PERSON] [13] introduced one of the earliest deepfake detection methods, focusing on unnatural eye blinking patterns, which prevailed in early-generation deepfakes. Similarly, [PERSON] et al. [14] proposed detecting inconsistencies in head pose and facial alignment. Although these hand-crafted approaches were initially successful, advances in generative architectures rapidly minimized such obvious artifacts, reducing the efficacy of these detection strategies. Deep learning-based approaches soon emerged as the dominant paradigm for deepfake detection. [PERSON] et al. [5] introduced MesoNet, a CNN-based model designed to detect manipulation artifacts at the mesoscopic level, highlighting the potential of automated feature extraction over manually crafted detectors. The XCeption network [8] has since become a mainstay in modern detection research due to its depthwise separable convolutions, which capture fine-grained spatial features with reduced computational overhead. [PERSON] et al. [11] demonstrated XCeption's high accuracy in detecting common deepfake artifacts (e.g., blending inconsistencies, subtle texture mismatches), although its computational footprint can pose challenges for real-time deployment. Residual networks (ResNets), introduced by [PERSON] et al. [9], are also widely adopted in deepfake detection. Their residual learning framework facilitates very deep models by mitigating the vanishing gradient problem. Variants such as ResNet-50 and ResNet-101 have been successfully employed for detecting manipulations [15], although multiple reports indicate that they often lag behind XCeption in scenarios involving subtle or high-resolution manipulations. The VGG architecture, particularly VGG16 [10], remains another fundamental CNN model. Its relatively uniform layer design has proven robust in image recognition tasks [16], facilitating the effective identification of manipulation artifacts. Yet, its higher computational demands and slower inference speed constrain its practical usage in cases where prompt decisions are necessary. In addition to CNN-based paradigms, emerging transformer-based solutions and domain adaptation techniques have shown promise in addressing the generalization gap between datasets [17; 18]. Ensemble learning approaches are also on the rise. For instance, [PERSON] et al. [19] propose an ensemble of XCeption and VGG16, demonstrating improved generalization by combining multiple architectures. Likewise, [PERSON] et al. [20] integrate deepfake detection with auxiliary tasks (e.g., emotion recognition) in a multi-task learning framework, aiming to bolster robustness. Despite these advancements, several critical challenges persist. One prominent concern is the generalization gap: models trained on one dataset often degrade in performance when applied to another [11].