# Explainable AI for DeepFake Detection
## 1 Introduction
Figure 1 shows the number of publications in the realm of explainable artificial intelligence for the last two decades and the plot shows how explainable artificial intelligence is blooming these days. This research deals with the reasoning behind the predictions made by the neural networks in classifying them as real or fake using explainable AI. Researchers and technology specialists have been working hard to build AI-based solutions for detecting deepfake content in response to this expanding threat. While progress has been made, there remains a critical issue to address: the explainability and limited transparency of deepfake detection systems. As the research landscape currently stands, existing deepfake detection systems are proficient at distinguishing between genuine and fake media. They can flag a video or image as authentic or manipulated with remarkable accuracy. However, what these systems struggle to provide is a coherent and interpretable explanation for their classification decisions. In essence, they can tell us whether a piece of content is real or a deepfake but fall short in elucidating the underlying rationale behind their judgment. This critical gap in the ability to explain deepfake detection outcomes is a pressing concern for several reasons. Firstly, without a clear understanding of why a particular piece of content is labeled as a deepfake, it becomes challenging to trust and rely on these systems in real-world applications. Secondly, explanations are essential for the forensic and legal aspects of deepfake detection. Being able to provide evidence-based explanations for the classification of content can be crucial in legal proceedings or when assessing the credibility of media in journalism. Interpretability is crucial for fostering trust among end-users and stakeholders, a key factor in the widespread adoption of deepfake detection technologies. Given these challenges, it has become clear that developing interpretation methods especially designed for neural networks is necessary. With the help of explainable artificial intelligence, this research proposal aims to provide explainability for deepfake detection models, thereby improving their reliability. To explore the explainability potential of a deepfake detection model, a multi-step process is essential. This process involves creating a model that classifies images into real or fake, and then incorporating explainable AI techniques to enhance the transparency of the model's reasoning process. The foundation of any deepfake detection model lies in the dataset used for training. To initiate the journey towards explainability, it is crucial to begin with a well-curated dataset that includes a diverse range of both real and fake images. This dataset acts as the foundation for training the model, enabling it to learn the distinctive features and patterns that differentiate genuine from manipulated content. In selecting an appropriate deepfake dataset, researchers must consider factors such as the variety of deepfake techniques, image quality, and ethical considerations surrounding the use of synthetic content. Achieving the optimal balance between realism and diversity in the dataset is crucial for training a robust model that can accurately detect subtle deepfake modifications. The trustworthiness of AI systems depends on their explainability, especially when they are tasked with critical functions such as deepfake detection. Once the model is trained to classify images, the next step is to enhance its transparency and interpretability. This is where explainable AI techniques come into play. The approach taken in this research draws inspiration from the network dissection algorithm proposed by previous researchers [9; 10; 11]. This research contributes to addressing the pressing challenges in deepfake detection by leveraging a deep learning model combined with explainable artificial intelligence (XAI) techniques.