## 1 Introduction  
The advent of deepfakes--synthetic or manipulated media that can depict either real or entirely fictitious individuals--generated using advanced artificial intelligence (AI) techniques--has profoundly transformed the digital media landscape. While many deepfake pipelines rely on generative adversarial networks (GANs) [1] and autoencoders, recent diffusion-based generative models further expand the complexity and realism of fabricated content. By employing state-of-the-art models, deepfakes enable the manipulation of images, videos, and audio to create highly realistic yet entirely fabricated media. This technology has been embraced for creative purposes in entertainment and education, but it also raises severe ethical and security concerns, including the dissemination of misinformation, erosion of public trust, and violations of personal privacy [2, 3]. The detection of deepfakes has emerged as a critical challenge in combating their misuse. Traditional detection methods, which rely on identifying visual artifacts, inconsistencies in motion, or statistical anomalies, have been outpaced by the rapid advancementsand diversity in deepfake generation techniques [3]. Modern deepfakes often exhibit minimal perceptual flaws, making manual detection nearly impossible. Even automated systems frequently struggle with issues such as generalization to new datasets and susceptibility to adversarial attacks--intentional perturbations that can deceive detection models [4]. These challenges highlight the urgent need for robust, generalizable, and efficient detection methods capable of adapting to evolving threats. Recent advancements in deepfake detection have focused on leveraging deep learning models, particularly convolutional neural networks (CNNs). CNNs excel at learning complex spatial and temporal features, making them highly effective for capturing subtle artifacts introduced by deepfake generation processes [5]. Nevertheless, transformer-based detectors and hybrid frameworks are emerging as well [6], and ensemble or domain-adaptation techniques could potentially mitigate performance drops across diverse datasets. Despite their promise, existing studies often have critical limitations. Many focus on a single architecture or dataset, failing to provide a comprehensive comparison of different models across diverse testing conditions. Additionally, few studies explore the tradeoffs between detection accuracy and processing speed or evaluate model robustness to adversarial perturbations [7]. These gaps hinder the practical deployment of deepfake detection systems in applications ranging from social media monitoring to forensic analysis. In this work, we present a broad evaluation of deepfake detection using subsets of frames extracted from two major datasets--the DeepFake Detection Challenge (DFDC) and FaceForensics++--to ensure representative yet manageable inputs for training and testing. This study addresses several limitations by conducting a comprehensive evaluation of three widely used deepfake detection models: XCeption [8], ResNet [9], and VGG16 [10]. These architectures were selected due to their established effectiveness in various image and video recognition tasks and their adaptability to the unique challenges posed by deepfake detection [11]. Our work aims to answer the following research questions:  
1. RQ1: How do XCeption, ResNet, and VGG16 models compare in terms of detection accuracy, precision, recall, and other performance metrics across diverse deepfake datasets? 2. RQ2: What is the generalization capability of these models when evaluated on multiple datasets, such as the DeepFake Detection Challenge (DFDC) and FaceForensics++? 3. RQ3: How resilient are these models to adversarial attacks designed to bypass detection systems? 4. RQ4: What are the trade-offs between detection accuracy and processing speed for each model, and how do these trade-offs influence their applicability for real-time detection versus forensic analysis?