# Comparison of Deepfake Detection Techniques through Deep Learning
## 1 Introduction
Deepfake media, however, leveraging powerful machine-learning (ML) techniques, have significantly improved the manipulation of the contents. Deepfake can be considered to be a type of splicing, where a person's face, sound, or actions in media is swiped by a fake target [8]. A wide set of cybercrime activities are usually associated with this type of manipulation technique, and while spreading them is easy, correcting the records and avoiding deepfakes are harder [9]. Consequently, it is becoming harder for machine-learning techniques to identify convolutional traces of deepfake generation algorithms, as there needs to be frequency-specific anomaly analysis. The most basic algorithms that were being used to train models for the task of deepfake detection such as Support Vector Machine (SVM), Convolution Neural Network (CNN), and Recurrent Neural Network (RNN) are now being coupled with multi-attentional [10] or ensemble [11] methods to \begin{table}
\begin{tabular}{l l c c} \hline \hline
**Type of Media** & **Examples** & **Intention to Mislead** & **Level of Truth** \\ \hline \multirow{3}{*}{Hoax} & Tampering evidence & \multirow{3}{*}{High} & \multirow{3}{*}{Low} \\  & Scam and Fraud & & \\  & Harming Credibility & & \\ \hline \multirow{3}{*}{Entertainment} & Altering movies & \multirow{3}{*}{Low} & \multirow{3}{*}{Low} \\  & Editing Special effects & & \\  & Art Demonstration & & \\ \hline \multirow{3}{*}{Propaganda} & Misdirection & \multirow{3}{*}{High} & \multirow{3}{*}{High} \\  & Political Warfare & & \\ \cline{1-1}  & Corruption & & \\ \hline Trusted & Authentic Content & Low & High \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Deepfake Information trust Table. increase the performance and address weakness of other methods. As proposed by [12], by implementing an ensemble of standard and attention-based data-augmented detection networks, the generalization issue of the previous approaches can be avoided. As such, it is of high importance to identify the most suitable algorithms for the backbone layers in multi-attentional and ensembled architectures. As generation of deepfake media only started in 2017, academic writing on the problem is meager [13]. Most of the developed and published methods/techniques are focused on deepfake videos. The main difference between deepfake video- and image-detection methods is that video-detection methods can leverage spatial features [14], spatio-temporal anomalies [15] and supervised domain [16] to draw a conclusion on the whole video by aggregating the inferred output both in time and across multiple faces. However, deepfake image-detection techniques have access to one face image only and mostly leverage pixel- [17] and noise-level analysis [18] to identify the traces of the manipulation method. Therefore, identifying the most reliable methods for face-image forgery detection that relies on convolutional neural networks (CNN) as the backbone for a binary classification task could provide valuable insight for the future direction in the development of deepfake-detection techniques. The overall approach taken in this work is illustrated in Figure 1.  
DenseNet has shown significant promise in the field of facial recognition. DenseNet as an extension of Residual CNN (ResNet) architecture has addressed the low-supervision problem of all its counterparts by initiating a between-layer connection using dense blocks. The dense blocks in the DenseNet architecture improve the learning process by leveraging a transition layer (essentially convolution, average pooling, and batch normalization between each dense block) that concatenates feature maps. As such, gradients from the initial input and loss function are shared by all the layers. The described implementation reduces the number of required parameters and feature maps, and consequently provides a less computationally expensive model. Therefore, we have decided to test DenseNet's capabilities and compare it with other neural network architectures.