# Explainable AI for DeepFake Detection
## 1 Introduction  
Deep learning technology has advanced considerably in recent years, with notable developments in the area of computer vision. Its applications span diverse fields, including finance, the automotive sector, wellness [1, 2, 3, 4], and healthcare, offering solutions to complex challenges. However, one downside of deep learning is its potential to create manipulated videos, images, and various multimedia forms, which create risks to privacy, national security, and democratic systems. With the widespread growth in modern technology concerning mobile phones and other editing tools, ease of access to social media platforms has made it convenient to create and distribute digital content [5, 6, 7]. These deceptive media are referred to as 'deepfakes'. Immense amounts of research in the computer vision domain, machine learning, and the considerable amount of data availability make it harder to differentiate between altered and real media content. This has detonated social media platforms with forged videos, images, and fake content that appears realistic [5]. Lately, this has imposed a significant threat to society, business, democracy, and public discussion. Within the past few years, the journalism industry has been struggling to separate real content from fake news [5, 6, 7]. It harms the cybersecurity and national security of individuals and organizations. As information and technology continue to evolve, the creation of deepfake content has become easier and it is an emerging concern in today's digital environment. There are different AI techniques and tools that help in creating these manipulated images. However, the creation of fake images is increasing to a higher level, which makes it difficult for observers to differentiate between fake and real images. Images generated by GANs (generative adversarial networks) have a wide range of applications, including text-to-image or image-to-image conversion, photo editing and blending, facial aging simulation, creating new human poses, generating 3D visuals, and restoring missing parts of photos through inpainting. However, it would be one-sided to dismiss the impact of deepfakes. GAN-generated images can convincingly fool the human eye. Additionally, several deepfake creation tools and software are publicly available, allowing users to produce highly realistic content. As deepfake generation algorithms advance rapidly, and the quality of these creations continues to improve, reliable detection tools have become increasingly important in today's society. A deep learning model is employed in this study to distinguish between authentic and fake instances. To construct the model, three distinct CNN architectures are utilized, allowing for a comparison of their effectiveness in detecting deepfakes. While detection accuracy remains a vital focus, the interpretability of deep learning models is equally important to ensure their widespread acceptance in real-world applications. Users, decision-makers, and stakeholders must understand why a particular instance is classified as fake or real. Without this understanding, the trustworthiness and adoption of these systems may be limited, particularly in critical domains such as journalism, law enforcement, and content moderation. Additionally, the lack of transparency could hinder forensic investigations and legal proceedings where evidence-based reasoning is required. To address these challenges, this research incorporates explainable artificial intelligence (XAI) techniques to provide clear and interpretable explanations for the decisions made by the deepfake detection models. By doing so, this study aims to enhance trust in the system's outputs and improve its practicality in addressing the societal risks posed by deepfakes. Explainable artificial intelligence stands out as an interesting field of study that has been emerging in recent times.