# Quick Overview of Face Swap Deep Fakes
## 3 Challenges  
Although neural networks adapt very well to new datasets, the provided algorithms solve or produce additional difficulties.  
Figure 2: Qualitative comparison of results of face swap models.  
Insufficient training data could be a potential issue. In the case of face synthesis, typically, significantly more training data is required for deep neural network algorithms. Developers of algorithms are searching for ways to separate universal features for the person from universal attributes for photo/video to exchange some of these characteristics. This strategy provides access to databases that do not require advanced labeling. Most developers utilize distinct training databases, which is not optimal for a performance comparison. One-to-one and autoencoder methods require one-of-a-kind, individual input, although training is faster than in other applications. As for training, many-to-many GANs train quicker than diffusion models, although they are not always superior in performance quality. The authors also recognize the significance of training data when employing pre-trained external models for identity extraction or face parsing. Instead of training it on the same dataset as the converter, developers utilize an adequately generalized model. Federated learning can help address the challenge of insufficient training data by allowing multiple users to collaborate and train models locally without the need to share their sensitive data, thereby enhancing performance and avoiding data privacy concerns [60].  
The intricacy of human faces and their occurrence of unique traits present a second difficulty. Algorithms based on a prepared model for extracting distinguishing features can miss them, producing aberrant deep fakes. One of the most challenging tasks for deep fake generation algorithms is to generate sufficiently realistic visuals to trick viewers. This generation demands sophisticated machine learning techniques and a comprehensive comprehension of human facial expressions, body motions, and behavior. The algorithm must learn how to accurately map a person's facial motions onto another person's face while preserving the same facial expressions, lip movements, and other minor features that make an image or video appear genuine. Realizing this level of realism is complex and demands a significant investment of time, data, and processing resources.  
The described methods lack built-in features that increase performance with access to more data during operation. It relies solely on one image sample. Developing these types of algorithms can enhance the quality of the generated samples.  
Another difficulty for deep fake creation algorithms is ensuring that the generated photos do not contain artifacts or flaws that could disclose their false character. This issue is crucial when generating images with a high degree of manipulation, as even slight errors can ruin the illusion and make the image or video appear false. In addition, the system must account for various lighting conditions, camera angles, facial expressions, occlusions, and other difficult circumstances.  
Faceswap algorithm development is complicated by the nature of deep fake detection technology, which constantly evolves. As new techniques and tools are produced, it becomes increasingly challenging to create a deep fake using traditional methods; therefore, new strategies must be developed to keep up with the most recent advancements.