# Explainable AI for DeepFake Detection
## 2 Related Works
As discussed in [6,7], [PERSON] has explored different datasets and model architectures, contributing to advancements in distinguishing between real and manipulated media, which are crucial for forensic applications and combating misinformation. [PERSON] [16] introduced an approach for quantifying the layer-wise representations learned by GAN models, which are used to generate scenes. The explainability module focuses on understanding the internal units of CNN models, which act as object detectors to classify different types of GAN-generated scenes. The quantitative and qualitative results from their research explain the features learned by training the model. [PERSON] [17] used XAI methods such as GradCAM and SHAP for explaining the predictions made by an EfficientNet model trained on a DFDC dataset consisting of manipulated videos. The results from the experiments show that human perceptions of deepfake explanation masks were not always in sync with objective metrics and other explainable artificial intelligence methods need to be considered for better results. [PERSON] [18] proposed a quantitative framework to estimate the informativeness and visual quality of explanations of deepfake classifiers. The paper highlights the need for intuitive explanations to validate deepfake detections, ensuring both user trust and compliance with legal frameworks such as GDPR. It evaluates existing explainable AI (XAI) methods, such as saliency maps, Grad-CAM, SmoothGrad, SHAP, and LIME, for their effectiveness in generating interpretable explanations for visual classifiers. They benchmark state-of-the-art video recognition models on popular datasets and explore how heatmap-based explanations can be communicated effectively to users, offering insights for improving the interpretability of deepfake detection systems. [PERSON] [19] proposed an FST-matching model (fake source target matching) for the interpretation of deepfake detection to learn the features of images that are used to categorize them as fake or real. [PERSON] [20] introduced an evaluation method for GANs through the XAI technique known as a logic learning machine, which has been applied to enhance data augmentation techniques. [PERSON] [21] proposed an effective and interpretable method known as a dynamic prototype network for explaining deepfake temporal artifacts. This paper introduces DPNet, an XAI (explainable AI) approach for deepfake detection that emphasizes interpretability and temporal dynamics. Unlike traditional methods, which primarily focus on frame-by-frame analysis and offer limited explanations, DPNet enhances transparency by learning  
Figure 2: Explainable Artificial intelligence (XAI) Approach [12]. the dynamic prototypes of temporal inconsistencies in videos. These prototypes serve as case-based evidence, allowing the model to explain why a particular prediction was made in a way that reflects its underlying computational process. Additionally, DPNet provides visual dynamic explanations by projecting learned prototypes onto representative video patches, making the detection process accessible and understandable for humans. [PERSON] [22] used the LIME XAI technique to interpret the CNN model used for identifying real and fake images, which also detailed the part of the image that caused it to make the specific classification. This paper focuses on deepfake detection using deep learning (DL) models, specifically convolutional neural networks (CNNs), and enhances their interpretability through explainable AI (XAI) methods, particularly local interpretable model-agnostic explanations (LIME). It examines the efficacy of different CNN models, including InceptionResNetV2, DenseNet201, InceptionV3, and ResNet152V2, in classifying real and deepfake images from a dataset containing 70k real images and 70k fake faces generated by StyleGAN. This approach combines high performance with XAI to provide a reliable and interpretable solution for deepfake detection.