# iCaps-Dfake: An Integrated Capsule-Based Model for Deepfake Image and Video Detection
## 2 Literature Review  
Previous attempts to detect deepfakes followed one of two general approaches: artifact-specific detectors which spot artifacts left by different creation methods or undirected approaches that tries to achieve generalization by applying different deep learning models. The artifact-specific models can be further categorized into spatial which are concerned with environment, forensics and blending based models or temporal artifacts which spots synchronization, behavior, coherence and physiology changes. The environment spatial artifact detection models depend on the aberrant content of the fake face when compared to its surroundings. For example, [PERSON] [31] addressed deepfake creations with low images resolutions that showed artifacts when wrapped to fit the target face by building a model that combined four CNN models: VGG16, ResNet50, ResNet101 and ResNet152. DSP-FWA [32] added to the FWA method [31] by using a pooling module to better detect the difference in resolutions of the target's face and their test results showed improvements over the FWA model. Another example was shown in [PERSON] et al. [33] where they made a network that first split the source image into face and hair/context, then generated three encodings of source, face alone and hair/context. The encoding of the source is then concatenated to the difference of the encoding of the other two elements and input to a decoder for classification. The model was tested on FaceForensics++ (FF++) [34], Celeb-DF [35] and DFDC-P [36] datasets and achieved comparable results. Forensics spatial detection models analyze fine features and patterns caused by the creation models. [PERSON] et al. [37] analyzed the camera's unique sensor noise, called photo response non-uniformity (PRNU), to detect stitched content. They created their own dataset consisting of 26 videos making their results very specific to handling noise generated by their own camera. In Two-branch [38], promising results were produced by [PERSON] et al. who focused on the residuals and proposed a recurrent network with two branches, one that amplifies the frequencies and enhances it while the other transfers the original information with fine tuning in the color domain. As a final example HeadPose [39] looked for imperfections rather than residuals and managed to apply a Support Vector Machine (SVM) model to detect inconsistent head poses in 3D. The final spatial detection technique is blending which looks for artifacts resulting from blending the face back onto the frame. In [40], the authors made a frequency analysis to emphasis artifacts to the learner using Discrete Fourier Transform (DFT) together with Azimuthal Average then fed the output to both supervised algorithms: Support Vector Machine (SVM) and Logistic Regression (LR), along with unsupervised algorithms K-means clustering. The authors tested their model on FF++ dataset. Temporal Lip-sync deepfakes can be detected by comparing the synchronization between speech and mouth landmarks as [41; 42] achieved. [PERSON] et al. [43] also exploited the irregularities in the dynamics of the mouth shape (visemes) and the spoken phonemes focusing on the letters M, B and P. Behavior anomalies can be detected as in [PERSON] et al. [44] where they trained a Siamese network with triplet loss to simultaneously train audio and video inputs and perceive emotions from them both for deepfake detection. Coherence detection tests the coherence between video frames and each other, some detectors used recurrent neural network (RNN) based models to predict fake videos. In [45], [PERSON] et al. detected flicker and jitter artifacts by applying a RNN, while [PERSON] et al. [46] applied an LSTM specifically on the area of the face and tested his model only on FF++. Physiological detection is based on the hypothesis that generated content will not have the same physiological signals as real one, [PERSON] et al.