# Comparison of Deepfake Detection Techniques through Deep Learning
## 2 Literature Review
### Deepfake Detection Algorithms
Holistic techniques are competent in learning human faces and extracting higher-dimensional semantic features for classification. Other techniques that leverage spatial features and spatio-temporal anomalies in the supervised domain such as Xception [48] and EfficientNet [49] have been shown to be more efficient than CNNs. Xception architecture claims to gain a more efficient use of model parameters due to depthwise separable convolutions that can understand as an inception module. [PERSON] and [PERSON] [16] demonstrated that Xception combined with metric learning can enhance the classification in high-compression scenarios. They were able to achieve an AUC score of 99.2% and accuracy of 90.71% for deepfake video identification on the Celeb-DF dataset. [PERSON] et al. [14] in their experimental analysis demonstrated that XceptionNet combined with an additional Bi-LSTM and LSTM layer can achieve a 79% ROC-AUC score. [PERSON] et al. [50] demonstrated that Xception does not have a good performance on face-image datasets (AUC of 73.2) and, furthermore, it has a high true-negative rate while having the lowest true-positive rate. To summarize, Xception may provide better performance for fake video detection; however, it does not address the generalizability issue across different datasets and does not perform well when fed with images only. EfficientNET proposes a new scaling method that uniformly scales all dimensions of depth/width/resolution using compound coefficient. [PERSON] et al. [15] were able to achieve an AUC of 0.95% and F1-score of 88% on the DFDC dataset. [PERSON] and [PERSON] [51] demonstrated that an increased scale in all dimensions may not always lead to higher accuracy due to the fact that CNNs will have to deal with more complex patterns that are difficult to transfer to a different task. [PERSON] et al. [52] were able to achieve a 96% accuracy on the FaseForensics++ dataset by making the complexity of detecting forged videos low using the depthwise separable convulsion of EfficientNet. In conclusion, Xception and EfficientNet, by uniformly scaling all dimensions, can gain a more efficient use of model parameters. Furthermore, they can extract spatial features and spatio-temporal anomalies by aggregating the inferred output both in time and across multiple faces due to their depthwise separable convolutions. These methods have illustrated that they can draw an improved conclusion on the whole video; however, they have not demonstrated any improvements to deepfake classification on a single image (i.e., deepfake image-detection). Recent scholarly work has been focused on implementing an ensemble of holistic and feature-based detection networks by addressing the drawbacks of both methods. [PERSON] et al. [53], in their work implementing a classification method, which involves a collection of classifiers with a certain utility function regarded as an aggregation operator, were able to achieve accuracy of 87%. [PERSON] et al. [12] were able to achieve a 92% accuracy on the DFDC dataset by implementing a hierarchical explainable forensics algorithm that incorporates humans in the detection loop. [PERSON] et al. [10] proposed a multi-attentional deepfake detection network that can achieve a 97% accuracy by implementing multiple spatial attention heads, textural feature enhancement blocks and aggregating low-level textural features and high-level semantic features. [PERSON] et al. [11] were able to achieve AUC of 87% on DFDC by assembling different trained Convolutional Neural Network (CNN) models that combined EfficientNetB4 with attention layers and Siamese training. [PERSON] et al. [54] demonstrated that a good balance between accuracy and efficiency can be achieved with two separated EfficientNet architectures that simultaneously analyze raw content and its frequency-domain representation.