# Deep Learning Methods to Detect Image Falsification
## 2 Related Work
### An Overview of Deepfake Detection Techniques  
The detection of deepfakes is a complex task, and there are various methods proposed to address this challenge. Table 1 provides a comparison of different deepfake detection methods that have been tested on different datasets. Most of these methods focus on the facial region where the manipulations are performed, and they rely on the fact that deepfake images are not entirely accurate and lack essential facial features, such as eyes, mouths, and noses. Additionally, some methods aim to identify various artifacts that occur due to compression or other digital processing of the video.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Author** & **Group** & **Detection Method** & **Dataset** & **Accuracy** & **AUC** \\ \hline [PERSON] et al. (2020) & Deepfake detection methods by analyzing image changes over time & Video and audio comparison over time & In-the-wild & 93.90\% & - \\ [PERSON] et al. (2020) & Deepfake detection methods by analyzing the face area & Analysis of biological facial signals & FaceForensics & 94.65\% & - \\ [PERSON] et al. (2020) & Other & Frequency domain analysis & FaceForensics++ & 90.00\% & - \\ [PERSON] et al. (2021) & Deepfake detection methods by analyzing the face area & Attention-based video authentication & Caleb-DF & - & 98.37\% \\ [PERSON] et al. (2021) & Deepfake detection methods by analyzing image changes over time & Facial changes over time & Face2 face & 97.39\% & - \\ [PERSON] et al. (2021) & Deepfake detection methods by analyzing image changes over time & Facial feature changes over time & FaceForensics++ & 99.70\% & 99.9\% \\ [PERSON] et al. (2022) & Deepfake detection methods by analyzing image changes over time & 3D face comparison over time & Celeb-DF & 99.78\% & - \\ [PERSON] et al. (2021) & Other & Image comparison based on hash values & UADFV & 88.01\% & - \\ [PERSON] et al. (2021) & Deepfake detection methods by analyzing the face area & Model trained on three different types of images & WPDD & 99.80\% & - \\ [PERSON] et al. (2022) & Deepfake detection methods by analyzing the face area & Attention-based detection & FaceForensics++ & 70.10\% & - \\ [PERSON] et al. (2022) & Other & Analysis of GAN spatial and frequency features & FaceForensics++ & 98.02\% & - \\ [PERSON] et al. (2023) & Deepfake detection methods over time & Video and audio comparison over time & FakeAVCeleb & 98.51\% & - \\ [PERSON] et al. (2023) & Deepfake detection methods by analyzing image changes over time & Video and audio comparison over time & FakeAVCeleb & 90.94 & - \\ [PERSON] et al. (2023) & Deepfake detection methods by analyzing the face area & Attention-based detection & FaceForensics++ & - & 98.57\% \\ [PERSON] et al. (2023) & Deepfake detection methods by analyzing the face area & Attention-based detection & Celeb-DF & - & 99.26\% \\ [PERSON] et al. (2023) & Deepfake detection methods by analyzing the face area & Attention-based detection & Deepfakes & - & 98.93\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: A comparison of deepfake detection methods.  
It is challenging to comprehensively evaluate the effectiveness of these methods since they have been tested on different datasets, making it difficult to compare and generalize the results. However, certain methods have demonstrated outstanding performance, achieving accuracy levels of over 95%. Notably, [PERSON] et al.'s approach stands out with a remarkable 99.9% AUC (Area Under the Curve). Therefore, it has been selected for further investigation.