# Deep Learning Methods to Detect Image Falsification
## 1 Introduction  
The use of deep learning techniques has made it possible to create highly realistic fake media, including images, videos, and audio. These deepfakes have opened up new possibilities for deception and the spread of false information. They can be used to manipulate public opinion, create fake news, and cause significant harm to individuals and society as a whole [1, 2]. Studies have shown that people are often unable to identify deepfakes, with an accuracy rate of only 50% [3]. Therefore, it is crucial to develop accurate and efficient methods for detecting deepfake videos. Detecting deepfakes is a challenging task compared to traditional data manipulation detection because the use of deep neural networks reduces manipulation traces. Approaches for deepfake image detection and video detection have slightly different approaches. Vision Transformer (ViT) is an architecture used in Natural Language Processing, but it has also been successfully applied to image classification. Typically, pretraining such a network requires a large number of images and computational resources. A solution presented in [4] proposed convolution-free transformers that were trained on a single computer. Additionally, a student-teacher strategy relies on distillation tokens, where the student learns through attention. Building upon this research, an improved ViT wasdeveloped [5] to extract both local and global features. The main issue with Convolutional Neural Networks (CNN) is their susceptibility to overfitting and misclassification due to their inability to detect the relationship of a local region as part of an image. By combining ViT, the data-efficient image transformers distillation method (DeiT), and patch embedding with CNN features, better deepfake detection results have been achieved compared to the state-of-the-art (SOTA) model. The main drawback is increased computational complexity. The most classical approach to deepfake detection is a binary classification problem. However, better results for deepfake detection have been achieved when it is defined as a fine-grained classification problem. In [6], a multi-attentional deepfake detection network is proposed, which consists of multiple specialized heads, a textural feature enhancement block, and feature aggregation. The proposed method achieved better results on the high-quality (HQ) version of the FaceForensics++ dataset but suffered with highly compressed videos due to the loss of textural information. Results for the DFDC dataset showed state-of-the-art performance, and it also outperformed existing solutions in terms of transferability. The four main types of deepfakes are entire face synthesis, reenactment, facial attributes manipulation, and face swap [7]. To date, various approaches have been proposed for detecting deepfake videos, including methods based on facial landmarks, inconsistencies in audio-visual cues, and analysis of the frequency domain [7; 8; 9; 10]. The use of biometric artifacts, such as eye, lip, and eyebrow features, has shown good results, but they are vulnerable because they require large amounts of training data and may provide incorrect results when dealing with people who have illnesses. Spatio-temporal features capture inconsistencies in motion, whereas pixel and statistical feature detection are other variations in hand-crafted feature-based extraction that show good results. However, these methods are also not immune to factors such as texture or compression rate [7]. Successful fake image detection requires high-quality training data, which are often limited. Publicly available fake image databases can be classified into first-generation databases and second-generation databases [11]. First-generation databases, such as the UADFV database, provide videos that are of lower quality compared to second-generation datasets. These datasets contain certain visual artifacts that are easily identifiable, such as varying color contrasts and visible boundaries of the fake masks.