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# Deepfake Detection Using Multiple Facial Features

Xinzhe Wang, Duohe Ma, Liming Wang, Zhitong Lu, Zhenchao Zhang, Junye Jiang

► To cite this version:

Xinzhe Wang, Duohe Ma, Liming Wang, Zhitong Lu, Zhenchao Zhang, et al. Deepfake Detection Using Multiple Facial Features. 19th IFIP International Conference on Digital Forensics (Digital-Forensics), Jan 2023, Arlington, VA, United States. pp.161-175, 10.1007/978-3-031-42991-0_9 . hal-05517476

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# Deepfake Detection Using Multiple Facial Features

Xinzhe Wang, Duohe Ma, Liming Wang, Zhitong Lu, Zhenchao Zhang and
Junye Jiang

Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China
maduohe@iie.ac.cn

**Abstract.** Deepfake digital forgery techniques leverage deep learning to replace faces and modify facial expressions in images and videos. The techniques have been used to produce fake pornography, spread fake news and rumors, influence public opinion and even elections. However, deepfake detection techniques are well behind deepfake generation technology.

This chapter describes a deepfake video detection method that leverages aspect ratios to express multiple facial features. The aspect ratios of facial features are computed for every frame in a video and a time window is used to segment processed frame sequences into multiple short segments, following which pattern matching is employed to identify abnormal expressions that are indicative of deepfakes. Experiments with the FaceForensics++ and Celeb-DF datasets reveal that the proposed method detects deepfake videos effectively. Moreover, the aspect ratio computations improve the ability to detect compressed deepfake videos.

**Keywords:** Deepfake Detection · Facial Feature Extraction · Aspect Ratios

## 1 Introduction

The Internet abounds with deepfake images and videos created by applying deep learning techniques to replace or modify the original faces and facial expressions. In the beginning, deepfakes were used to target female celebrities [6]. However, modern deepfake technologies have advanced significantly by leveraging deep adversarial networks to produce high-quality fake videos that appear to be real to human eyes [33]. Deepfake forgery techniques are being used to produce fake pornography, spread fake news and rumors, influence public opinion and even elections [14].

Several researchers have focused on detecting deepfake videos [24, 25, 32, 36]. However, while the detection methods differ, they share the common shortcoming of focusing on a specific facial feature, which renders deepfake detection inefficient.

Since the clues provided by a single facial feature are limited, multiple facial features should be employed to enhance deepfake video detection. This chapter
proposes a detection method that employs aspect ratios to capture multiple facial features uniquely and easily. Multiple facial features comprise several single facial features such as eye blinking, lip movement and nasal flaring that are essential to deepfake detection.

The proposed method computes the aspect ratios of multiple facial features for every frame in a video and uses a time window to segment processed frame sequences into multiple short segments, following which pattern matching is employed to identify abnormal expressions that are indicative of deepfakes. Experiments with the FaceForensics++ and Celeb-DF datasets reveal that the proposed method detects fake videos more effectively than other deep-learning-based detection methods [4, 8, 17, 19, 25, 29]. Additionally, using the aspect ratio computations improves the ability to detect compressed deepfake videos.

# 2 Related Work

This section discusses related research on deepfake generation, deepfake video detection and aspect ratios.

## 2.1 Deepfake Generation

During the past four years, several researchers have employed deep learning to generate fake faces [2, 13, 26, 30]. In particular, the application of generative adversarial networks has greatly advanced the creation of deepfakes [13, 26]. Yang et al. [35] developed a technique that changes faces while improving the resolution of the facial images.

Methods for creating deepfakes fall into three main categories, facial expression manipulation, facial part manipulation and generative adversarial networks. Expression manipulation involves changing a facial expression without changing the face. Facial part manipulation involves replacing a face with another person's face. Generative adversarial network methods create nonexistent persons with random facial expressions.

Deepfakes are mainly based on facial expressions and facial features. Due to the characteristics of deep learning, parts of a face that have insignificant movements are rarely processed. Some improvements have been made to deepfakes, as in the case of the FaceSwap-GAN method [16, 21], which adds adversarial loss and perceptual loss on an original basis. As a discriminator, the adversarial loss makes the generated image close to the real image. Perceptual loss can make the direction of eye rotation more real, which creates a fake face of higher quality.

## 2.2 Deepfake Video Detection

Deepfake video detection algorithms are broadly divided into two categories, frame-by-frame detection and interframe association detection.
**Frame-by-Frame Detection** Frame-by-frame detection identifies fake facial features in single video frames. Some of the detection methods focus on simple features that are selected manually. For example, Matern et al. [22] focus on simple artificial defects such as colors, wired shadows on faces and missing details of the eyes and teeth.

Other frame-by-frame detection methods leverage deep features extracted by
deep convolutional neural networks. Chollet [5] successfully modified Xception, a
deep learning method with depthwise-separable convolutions, to the task of de-
tecting deepfakes. Afchar et al. [1] developed the MesoNet method that leverages
mesoscopic properties of images to detect deepfakes. Li et al. [17] employed an
advanced architecture named HRNet [28] that detects deepfakes by examining
the blending boundaries of manipulated images. These methods employ pow-
erful convolutional neural networks to obtain good detection performance, but
they require significant computational resources. However, their principal defi-
ciency is that deepfake detection is based on a single feature, which is difficult
to reproduce and lacks robustness.

**Interframe Association Detection** Interframe association detection exam-
ines forged features in series of frames to identify deepfakes. Li et al. [18] lever-
aged abnormalities in eye blinking frequencies to identify deepfake videos. Yang
et al. [34] used outer and central landmarks to capture head and face directions,
respectively; inconsistencies in the two directions are indicative of deepfakes.
However, these detection methods employ manually-selected features, which ren-
ders them less discriminative and limits their performance.

To address the limitation, researchers have developed appearance-based deep-
fake detection methods that engage expressive deep temporal features. The
method of Guerra and Delp [10] employs a convolutional neural network for fea-
ture extraction from frames and a long short-term memory network to process
feature sequences. Sabir et al. [25] employed a similar architecture, but replaced
the long short-term memory network with a bidirectional gated recurrent unit.
However, these deepfake detection methods, like the frame-by-frame detection
methods, rely on convolutional neural networks and, therefore, have the same
limitations.

2.3 Aspect Ratio

The aspect ratio, which is the ratio of the maximum vertical distance to the
maximum horizontal distance of an image region, is employed to define the res-
olutions of electronic device display screens. The eye aspect ratio (EAR) was
proposed by Soukupova and Cech [27] to detect eye blinks. Figure 1 shows the
landmarks on a human eye that are used to compute the eye aspect ratio. The
eye aspect ratio was used by Houssaini et al. [11] to detect automobile driver
fatigue in real time.

Eye aspect ratios have the potential to record facial features and represent
them uniquely, effectively capturing the states of the eyes in facial images. The
<figure><img src="image_4.png" /><figcaption>Fig. 1. Human eye landmarks.</figcaption></figure>

aspect ratios are easy to compute and are affected very little by distance and image resolution.

The eye aspect ratio EAR is computed as [27]:

$$ EAR = \frac{\|p_2 - p_6\| + \|p_3 - p_5\|}{2\|p_1 - p_4\|} $$

where $p_1$ and $p_4$ are the locations of the landmarks at the extreme right and left corners of the eye in Figure 1, $p_2$ and $p_3$ are the locations of the landmarks at the top right middle and top left middle of the eye, and $p_5$ and $p_6$ are the locations of the landmarks at the bottom right middle and bottom left middle of the eye.

The proposed deepfake detection method employs aspect ratios computed for seven important facial components, jaw, right eyebrow, left eyebrow, nose, right eye, left eye and mouth. Collectively, the seven aspect ratios concisely and effectively capture the facial expressions of humans.

# 3 Deepfake Detection Method

Figure 2 shows the proposed deepfake detection method. It involves three steps, data pre-processing, aspect ratio (AR) computation and classification.

## 3.1 Data Pre-Processing

The data pre-processing step chops a suspected deepfake video into clips, identifies the facial regions and marks key landmarks to obtain high-quality facial features from the facial images. In particular, the data pre-processing step identifies facial regions and their landmarks, and aligns the landmarks.

Facial region detection identifies the regions of interest in the video clips [31]. Next, 68 facial landmarks related to various facial features are identified and marked. Following this, the landmark points are aligned to obtain the best detection positions using an affine transformation, yielding 32 landmark points to compute the aspect ratios of the seven facial features. Finally, the input video is segmented into frames of fixed length to enable frame-level detection.
<figure><img src="image_5.png" /><figcaption>Fig. 2. Deepfake detection method.</figcaption></figure>

## 3.2 Aspect Ratio Computation

The deepfake detection method employs aspect ratios computed for seven important facial components, jaw, right eyebrow, left eyebrow, nose, right eye, left eye and mouth. The data pre-processing step yields seven facial feature vectors with 32 landmark points:

$$ \text{J-AR(Jaw)} = \frac{\|p_1 - p_9\| + \|p_{17} - p_9\|}{2\|p_1 - p_{17}\|} $$

$$ \text{REB-AR(Right Eyebrow)} = \frac{\|p_{20} - p_{18}\| + \|p_{20} - p_{22}\|}{2\|p_{18} - p_{22}\|} $$

$$ \text{LEB-AR(Left Eyebrow)} = \frac{\|p_{25} - p_{23}\| + \|p_{25} - p_{27}\|}{2\|p_{23} - p_{27}\|} $$

$$ \text{N-AR(Nose)} = \frac{\|p_{28} - p_{33}\| + \|p_{28} - p_{35}\|}{2\|p_{32} - p_{36}\|} $$

$$ \text{RE-AR(Right Eye)} = \frac{\|p_{38} - p_{42}\| + \|p_{39} - p_{41}\|}{2\|p_{37} - p_{40}\|} $$

$$ \text{LE-AR(Left Eye)} = \frac{\|p_{44} - p_{48}\| + \|p_{45} - p_{47}\|}{2\|p_{43} - p_{46}\|} $$

$$ \text{M-AR(Mouth)} = \frac{\|p_{51} - p_{59}\| + \|p_{53} - p_{57}\|}{2\|p_{49} - p_{55}\|} $$
where J-AR, REB-AR, LEB-AR, N-AR, RE-AR, LE-AR and M-AR are the aspect ratios corresponding to the jaw, right eyebrow, left eyebrow, nose, right eye, left eye and mouth facial components.

The aspect ratios of the seven facial components computed for each frame are incorporated in feature vectors $V$:

$$V = [v_{J-AR}, v_{REB-AR}, \dots, v_{M-AR}]$$

## 3.3 Classification

The feature vectors $V$ created in the aspect ratio computation step are input to a trained recurrent neural network for classification. First, each aspect ratio value sequence is segmented according to the size of the time window. Next, the recurrent neural network performs aspect ratio value repeatability matching on each segmented sequence. If different segmented sequences match multiple times, then the corresponding portions of the video are most likely fake. If the matching of the $j^{th}$ slice is confirmed, the corresponding similarity matching score $s_j$ of the slice is computed; otherwise, $s_j$ is set to zero. The fake score $S$ is computed by summing the individual similarity matching scores $s_j$ over all $n$ slices:

$$S = \sum_{j=1}^{n} s_j$$

The larger the fake score $S$, the greater the likelihood that the video is a deepfake. After much experimentation, a fake score $S$ of 12 was set as a threshold to deem a video to be a deepfake.

# 4 Experiments and Results

This section describes the experiments and results.

## 4.1 Datasets

Two datasets, FaceForensics++ (FF++) and Celeb-DF, were employed in the experiments. FaceForensics++ [24] is a large fake face dataset that is widely used by researchers. It contains more than 1,000 real videos along with their fake versions created using four face-changing algorithms, Face2Face, FaceSwap, Deepfakes and Neural-Textures. However, in the experiments, only fake videos created using the Face2Face, FaceSwap and Deepfakes algorithms were employed.

Each video in FaceForensics++ has an original version (raw), slightly compressed version (c23) and heavily compressed version (c40). The average video length in the FaceForensics++ dataset is 13 seconds. For experimental efficiency and time accuracy, 800 videos with clear and complete faces were selected. The first ten seconds of each video was used as experimental data.
<table><caption>**Table 1.** General evaluation of methods.</caption><thead><tr><th>Method</th><th>FaceForensics++(raw)<br>AUC Score</th><th>Celeb-DF<br>AUC Score</th></tr></thead><tbody><tr><td>XceptionNet</td><td>92.4%</td><td>64.7%</td></tr><tr><td>CNN+RNN</td><td>90.1%</td><td>72.1%</td></tr><tr><td>LRNet</td><td>96.9%</td><td>63.7%</td></tr><tr><td>DSP-FWA</td><td>94.8%</td><td>65.1%</td></tr><tr><td>ID-Reveal</td><td>93.7%</td><td>72.6%</td></tr><tr><td>Face X-Ray</td><td>95.7%</td><td>72.9%</td></tr><tr><td>Proposed Method</td><td>96.3%</td><td>73.3%</td></tr></tbody></table>

The Celeb-DF dataset [20] comprises 590 real videos and more than 5,000 fake videos, all of them created by the Deepfakes algorithm. Color conversion and edge softening algorithms were used to optimize the differences between synthetic and original faces, which reduced the synthetic boundary artifacts. This also reduced the jitter of the synthetic videos. The average video length in the Celeb-DF dataset is 11.8 seconds. For experimental efficiency and time accuracy, 150 real videos and 1,500 fake videos were selected. The first ten seconds of each video was used as experimental data.

## 4.2 Parameters

The Dlib toolkit was used in the data pre-processing step to chop faces and mark landmarks. The fully-connected layers of the recurrent neural network with 64 and 2 units were connected to the output layer. A dropout layer in the recurrent neural network with a dropout rate of 0.2 was inserted between the input and recurrent neural network. Another three dropout layers with dropout rates of 0.4 separated the remaining layers. The length of the time window was set to six. Each video was segmented into clips with a fixed length of 60. The classification model was trained over 400 epochs.

## 4.3 Comparative Evaluation

The proposed method was compared against six state-of-the-art deepfake detection methods, XceptionNet [5], CNN+RNN [25], LRNet [29], DSP-FWA [19], ID-Reveal [8] and Face X-Ray [17].

Table 1 shows the best-performance area under curve (AUC) scores for the seven methods using the FaceForensics++(raw) and Celeb-D datasets. In the case of the FaceForensics++(raw) dataset, the proposed method yielded the second highest score of 96.3%, just 0.6% less than the LRNet method. LRNet employed a special data pre-processing step to improve its detection rate. This involved marking the facial feature points to obtain the complete facial features and then using a marking point correction method based on optical flow to obtain
<figure><img src="image_6.png" /><figcaption>Fig. 3. Area under curve scores for uncompressed and slightly compressed videos.</figcaption></figure>

accurate facial features. However, the method required each video to be corrected twice frame by frame, which is much more computationally expensive than simply computing the aspect ratios as in the proposed method. Additionally, since LRNet mainly relies on pixel information, its detection performance drops for highly compressed videos. LRNet is also less effective at detecting high-quality deepfake videos because of the small differences in correction changes.

Table 1 also shows the best performance area under curve scores for the seven methods using the Celeb-DF dataset. The proposed method yielded the highest area under curve score of 73.3% for Celeb-DF dataset.

The ability of the proposed method to handle compressed videos was also evaluated; this is something that has been overlooked by most current methods. The evaluation compared the performance of the proposed method against four state-of-the-art methods, MesoNet [1], XceptionNet [5], LRNet [29] and ID-Reveal [8]. The detection models for all the methods were trained using uncompressed videos from the FaceForensics++ (raw) dataset and tested using slightly compressed videos from the FaceForensics++ (c23) dataset.

Figure 3 shows the area under curve scores (as fractions instead of percentages) for uncompressed and slightly compressed videos. LRNet and the proposed method have the best scores, however, the reduction in detection performance for slightly compressed videos is less for the proposed method (0.12) than for LRNet (0.17).

Next, the proposed method was compared against XceptionNet [5] to verify the training effect on the performance using different datasets. Specifically, training was conducted using Face2Face, FaceSwap, Deepfakes videos of three
<table><caption>**Table 2.** Performance on three independent datasets in FaceForensics++.</caption><thead><tr><th>Deepfake Video Proposed Method</th><th>XceptionNet</th></tr></thead><tbody><tr><td>Deepfakes(c0)</td><td>96.3%</td></tr><tr><td>FaceSwap(c0)</td><td>94.5%</td></tr><tr><td>Face2Face(c0)</td><td>98.7%</td></tr><tr><td>Deepfakes(c23)</td><td>95.1%</td></tr><tr><td>FaceSwap(c23)</td><td>95.0%</td></tr><tr><td>Face2Face(c23)</td><td>93.6%</td></tr><tr><td>Deepfakes(c40)</td><td>94.3%</td></tr><tr><td>FaceSwap(c40)</td><td>93.6%</td></tr><tr><td>Face2Face(c40)</td><td>93.1%</td></tr></tbody></table>

different compression factors and the performance was evaluated using three independent datasets from FaceForensics++.

Table 2 shows the results. The methods yield different accuracy values for different compression factors. As expected, the accuracy values drop as the compression factor increases because image details are lost. However, unlike XceptionNet, the proposed method yields greater than 93% accuracy even for highly compressed c40 videos. This demonstrates the robustness of deepfake detection when aspect ratios are used to capture facial features.

## 4.4 Analysis

Figure 4 shows the fake score values *S* of real and fake video images. The fake score values computed using aspect ratios tend to be more stable for fake images than real images.

Figure 5 compares the pattern matching results for real and fake video images. The area under score values tend to be more periodic for fake images than real images.

In order to understand the influence of facial features in deepfake detection, the detection performance with individual features and groups of features were evaluated using the FaceForensics++ (raw) dataset. Table 3 shows that using more facial features improves detection performance because more information can be matched. Additionally, integrating the two parts of the left and right eyes as one, and doing the same with eyebrows, and the nose or jaw appear to have less effect on detection performance. Clearly, the eyes and mouth are important features because they provide more information that is relevant to deepfake detection.

The effect of input length on detection performance was also evaluated. In this case, the input length corresponds to the number of frames provided to the detection model. Figure 6 shows the area under curve scores for different input
Real

<figure><img src="image_7.png" /></figure>

S = 8

<figure><img src="image_8.png" /></figure>

S = 11

<figure><img src="image_9.png" /></figure>

S = 7

<figure><img src="image_10.png" /></figure>

S = 6

<figure><img src="image_11.png" /></figure>

S = 7

Fake

<figure><img src="image_12.png" /></figure>

S = 22

<figure><img src="image_13.png" /></figure>

S = 27

<figure><img src="image_14.png" /></figure>

S = 17

<figure><img src="image_15.png" /></figure>

S = 22

<figure><img src="image_16.png" /></figure>

S = 23

<figure><img src="image_17.png" /><figcaption>Fig. 5. Pattern matching between real and fake video images.</figcaption></figure>

lengths. Because input length can affect detection performance when using the same dataset for training and testing, a suitable input length can improve the effectiveness and robustness of different data distributions. Figure 6 shows that an input length of 60 yields good detection performance and is also efficient.

The length of the time window is also an important factor in detection. The time window length corresponds to number of frames in a short frame sequence. The time window length is at least one and is at most equal to on-half of the
<table><caption>**Table 3.** Area under curve scores for various combinations of facial features.</caption><thead><tr><td>Facial Features</td><td>AUC Score</td></tr></thead><tbody><tr><td>Eyes</td><td>68.7%</td></tr><tr><td>Mouth</td><td>73.9%</td></tr><tr><td>Nose</td><td>28.4%</td></tr><tr><td>Jaw</td><td>30.2%</td></tr><tr><td>Eyebrows</td><td>27.1%</td></tr><tr><td>Eyes and Mouth</td><td>82.8%</td></tr><tr><td>Eyes and Nose</td><td>70.1%</td></tr><tr><td>Eyes and Jaw</td><td>70.1%</td></tr><tr><td>Mouth and Nose</td><td>74.1%</td></tr><tr><td>Mouth and Jaw</td><td>83.3%</td></tr><tr><td>Eyes, Mouth and Jaw</td><td>84.3%</td></tr><tr><td>Eyes, Mouth and Nose</td><td>86.6%</td></tr><tr><td>Eyes, Mouth and Eyebrows</td><td>82.2%</td></tr><tr><td>All Features</td><td>96.3%</td></tr></tbody></table>

<figure><img src="image_18.png" /><figcaption>**Fig. 6.** Area under curve scores for various input lengths.</figcaption></figure>

length of the facial feature vector. Figure 7 shows the area under curve scores for different time window lengths. A time window length of six or seven yields good detection performance. In fact, six was chosen because it exactly divides the total length.

# 5 Discussion

Although deepfake video detection has been the subject of considerable research, certain problems remain to be addressed. One problem relates to video compression.
<figure><img src="image_19.png" /><figcaption>Fig. 7. Area under curve scores for various time window lengths.</figcaption></figure>

sion. Videos on the Internet have varying compression factors and experiments have shown that increased compression negatively impacts deepfake detection performance. The experiments in Section 4 demonstrate that a model trained using low compression data can handle high compression data, but the detection performance is reduced considerably. The noise flow model explored in this research reduces the gap to some extent, but it cannot be eliminated. Even if the H.264 encoding method is not used, different compression factors create large gaps between training and testing. Of course, if a video is highly compressed, its quality is reduced considerably, which would affect the dissemination of deepfake videos [3, 9]. Compression solutions cannot rely solely on data-driven methods, so a trained detection model will depend strongly on the compression of data in the dataset and the generalization ability would be poor. The noise extraction or residual of splicing detection used in traditional forensics would also be compressed, hindering feature extraction [7, 12]. Therefore, it is important to extract features that coexist under different compression factors.

A second problem deals with video resolution. Videos of different resolutions on the web have different face sizes. Large face sizes pose challenges to model testing [15]. After a face is uniformly scaled, the original features are lost to some extent, so that the features used for classification and detection are inconsistent with the distribution method, which reduces the detection rate.

A third problem is algorithm tampering. New generation methods constantly emerge and the drawback of data-based training methods is that they are not robust enough to handle deepfakes created by unknown tampering methods [23]. This is because the distributions of tampered features for different tampering methods are different, making a learning-based model too focused on local features and unable to adapt to the distributions of other tampering methods [22]. One solution is to expand the training set and continuously incorporate new tam-
pering methods as employed in the experiments described in Section 4. While
this approach may not handle unknown tampering methods effectively, it can at
least detect deepfakes created by as many known tampering methods as possible.

6 Conclusions

Deepfake technologies have advanced significantly by leveraging deep adversarial
networks to produce high-quality fake videos that appear to be real to human
eyes. Deepfake image and video detection have been the focus of considerable
research, but most detection methods engage single facial features with lim-
ited clues that reduce deepfake detection performance. The detection method
described in this chapter employs aspect ratios to capture multiple facial fea-
tures uniquely and effectively. The multiple facial features comprise several single
facial features such as eye blinking, lip movement and nasal flaring that are es-
sential to deepfake detection. The detection method computes the aspect ratios
of multiple facial features for every frame in a video and uses a time window
to segment processed frame sequences into multiple short segments, following
which pattern matching is employed to identify abnormal expressions that are
indicative of deepfakes. Experiments using the FaceForensics++ and Celeb-DF
datasets reveal that the proposed method detects fake videos more effectively
than other deep-learning-based detection methods. Additionally, using the aspect
ratio computations improves the ability to detect compressed deepfake videos.

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