# Deep Learning Methods to Detect Image Falsification
## 2 Related Work
### Deepfake Detection Methods by Analyzing the Face Area  
The presented article by [PERSON] et al. [9] focused on a detail-based digital video authentication system called ADD, which included three main components: localized discriminative feature extraction, detail-based data augmentation, and a classifier to distinguish between original frames and deepfakes. The article emphasized that deepfake generation algorithms mostly manipulate facial areas and leave the background untouched. Therefore, focusing on the face region, rather than analyzing the whole frame, improved the detection performance by reducing background noise and computation time, thus reducing the input size. Face regions were collected using facial recognition technologies, and the obtained face orientations were used to crop, align, and transform the faces into a standardized configuration. These cropped frames, which contained only facial regions, were further used for detail-based image enhancement and feature extraction. The ADD framework, while effective in boosting detection performance, may face limitations in terms of computational complexity, dependence on baseline models, generalizability to other datasets or evolving deepfake techniques, and robustness against adversarial attacks or countermeasures. [PERSON] et al. [10] proposed detecting natural biological signals hidden in deepfakes, as these signals are not easily replicable by generative models, and they play an essential role in identifying authentic content. The authors used a fixed-length video segment containing facial regions and extracted biological signals from those regions. They transformed these signals into different domains, such as time, frequency, and time-frequency, to analyze their relationships. An authentic classification model was then developed using the extracted information, which worked in a high-dimensional feature space. A drawback of the model was that the used dataset structure may introduce biases towards known generative models, which could affect the generalizability of the detector and potentially decrease its accuracy in \"in the wild\" cases. [PERSON] et al. [21] introduced a large-scale dataset called WPDD, containing various political figures' videos, for improving deepfake detection algorithms. The authors proposed a three-stream architecture for detecting deepfakes, which combined information from different image views to identify fake content. The first stream used the original image to learn general features, such as head shape and hair color. The second stream used a faded image to analyze skin color, whereas the third stream focused on facial features, using a sharpened input image. The authors used pre-trained CNN models to train each stream, and the streams were combined to obtain the classification result. The drawbacks of the proposed multi-stream deepfake detection method included: (1) the reliance on pre-trained VGG16, VGG19, and ResNet18 models, which could limit the method's adaptability to new types of manipulations or more advanced deepfake techniques, and (2) the evaluation of the method was based on the WPDD, which may not have been representative of diverse real-world scenarios, potentially affecting the generalizability of the method's performance. [PERSON] et al. [22] proposed a feature extraction model that identified the remaining artifacts in deepfake images using an attention-based mechanism. The authors used the MTCNN algorithm to extract facial regions from video frames, which were then passed through the pre-trained Xception model to obtain feature maps. A visual attention mechanism was applied to focus on the remaining manipulations in the image, which could help detect fake content. The proposed model reduced computation time by using a detail-based approach, as opposed to more computationally intensive mechanisms. However, this method struggled with certain types of deepfakes, misinterpreted some real features as inconsistencies, faced challenges with compressed data, and still had room for further performance improvement.