# Deep Learning Methods to Detect Image Falsification
## 2 Related Work  
### Deepfake Detection Methods by Analyzing Image Changes over Time  
[PERSON] et al. [16] proposed a method that monitored how a face changed over time in video recordings to detect if it has been manipulated. The proposed method involved dividing the video into frames and then extracting the face from each frame using the MTCNN tool. The extracted faces were then processed using various facial features, and the differences between the frames were computed. A classification process was then applied to detect anomalies from the processed data. The proposed method demonstrated comparable detection accuracy to other existing deepfake detection methods. While the study utilized a similar dataset size as other papers, the academic dataset used had limited quality and diversity. Consequently, the effectiveness of the proposed method may have been compromised when applied to high-quality datasets. [PERSON] et al. [17] proposed a method for detecting deepfake videos by analyzing the mismatch between real and manipulated videos over time. They suggested a method based on a 3D convolutional neural network that learned from the spatial and temporal information obtained from a sequence of frames. They used a 3D CNN to extract the features from a short sequence of frames and classified the videos as real or manipulated based on these features. Although, as the spatial inconsistency feature was dependent on the manipulation methods, frame-based methods had a tendency to overfit to specific forged patterns. Therefore, neglecting the temporal cues in this context would ultimately result in a lack of generalization capability. Another method proposed by [PERSON] et al. [18] analyzed both audio and visual data to detect deepfakes. They used lip-sync errors, where the mouth movements did not correspond to the spoken words, to identify manipulated videos. The proposed method involved analyzing the vertical intensity profile extracted from the mouth region. This intensity profile was expected to vary significantly when the mouth was open or closed or when there were changes in the skin tone, such as the teeth being exposed. However, the proposed method did not look at all possible spoken phonemes, which could further improve the model. [PERSON] et al. [8] proposed a method that utilized the temporal information of facial features to detect deepfake videos. They proposed the LRNet architecture that included a face processing module, a calibration module, a face embedding module, and an RNN classification model. The LRNet architecture was designed to extract geometric features from the face images and to detect abnormal facial movements that did not align with the temporal patterns observed in real videos. Although geometric features were more resilient and inexpensive, they were also more difficult to apply in a universal manner. This meant that there was a trade-off between their performance, particularly their ability to generalize and their computational cost. [PERSON] et al. [19] proposed a deepfake video detection system that extracted video frames and audio from the given video. Spatial feature extraction was performed using an improved XceptionNet model, and deep time-frequency features from the audio modality were extracted using the InceptionResNetV2 model. In this process, the feature representations extracted from these modalities were combined. This resulted in a feature vector representation of the entire video. These three levels of representation were individually processed by the Gated Recurrent Unit (GRU)-based attention mechanism, aiding in the learning and extraction of profound and significant temporal information for each level. Following this, the system evaluated if the forgery pertained only to video frames, audio,or both and subsequently delivered a final verdict regarding the video's authenticity. A variety of optimization algorithms could be leveraged to boost the efficacy of the suggested deepfake video detection method.