“Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks”の学習報告

Current face forgery detection methods achieve high accuracy under the within-database scenario where training and testing forgeries are synthesized by the same algorithm. However, few of them gain satisfying performance under the cross-database scenario where training and testing forgeries are synthesized by different algorithms. In this paper, we find that current CNN-based detectors tend to overfit to method-specific color textures and thus fail to generalize. Observing that image noises remove color textures and expose discrepancies between authentic and tampered regions, we propose to utilize the high-frequency noises for face forgery detection. We carefully devise three functional modules to take full advantage of the high-frequency features. The first is the multi-scale high-frequency feature extraction module that extracts high-frequency noises at multiple scales and composes a novel modality. The second is the residual-guided spatial attention module that guides the low-level RGB feature extractor to concentrate more on forgery traces from a new perspective. The last is the cross-modality attention module that leverages the correlation between the two complementary modalities to promote feature learning for each other. Comprehensive evaluations on several benchmark databases corroborate the superior generalization performance of our proposed method.

/pdf/generalizing-face-forgery-detection-with-high-frequency-1bwxpnraf5.pdf

Generalizing Face Forgery Detection with High-frequency Features

Although vanilla Convolutional Neural Network (CNN) based detectors can achieve satisfactory performance on fake face detection, we observe that the detectors tend to seek forgeries on a limited region of face, which reveals that the detectors is short of understanding of forgery. Therefore, we propose an attention-based data augmentation framework to guide detector refine and enlarge its attention. Specifically, our method tracks and occludes the Top-N sensitive facial regions, encouraging the detector to mine deeper into the regions ignored before for more representative forgery. Especially, our method is simple-to-use and can be easily integrated with various CNN models. Extensive experiments show that the detector trained with our method is capable to separately point out the representative forgery of fake faces generated by different manipulation techniques, and our method enables a vanilla CNN-based detector to achieve state-of-the-art performance without structure modification. Our code is available at https://github.com/crywang/RFM.

/pdf/representative-forgery-mining-for-fake-face-detection-4uu603dcbh.pdf

Representative Forgery Mining for Fake Face Detection

On existing public benchmarks, face forgery detection techniques have achieved great success. However, when used in multi-person videos, which often contain many people active in the scene with only a small subset having been manipulated, their performance remains far from being satisfactory. To take face forgery detection to a new level, we construct a novel large-scale dataset, called FFIW 10K , which comprises 10,000 high-quality forgery videos, with an average of three human faces in each frame. The manipulation procedure is fully automatic, controlled by a domain-adversarial quality assessment network, making our dataset highly scalable with low human cost. In addition, we propose a novel algorithm to tackle the task of multi-person face forgery detection. Supervised by only video-level label, the algorithm explores multiple instance learning and learns to automatically attend to tampered faces. Our algorithm outperforms representative approaches for both forgery classification and localization on FFIW 10K , and also shows high generalization ability on existing benchmarks. We hope that our dataset and study will help the community to explore this new field in more depth.

/pdf/face-forensics-in-the-wild-59bpi8tshb.pdf

Face Forensics in the Wild

Detecting digital face manipulation has attracted extensive attention due to fake media's potential harms to the public. However, recent advances have been able to reduce the forgery signals to a low magnitude. Decomposition, which reversibly decomposes an image into several constituent elements, is a promising way to highlight the hidden forgery details. In this paper, we consider a face image as the production of the intervention of the underlying 3D geometry and the lighting environment, and decompose it in a computer graphics view. Specifically, by disentangling the face image into 3D shape, common texture, identity texture, ambient light, and direct light, we find the devil lies in the direct light and the identity texture. Based on this observation, we propose to utilize facial detail, which is the combination of direct light and identity texture, as the clue to detect the subtle forgery patterns. Besides, we highlight the manipulated region with a supervised attention mechanism and introduce a two-stream structure to exploit both face image and facial detail together as a multi-modality task. Extensive experiments indicate the effectiveness of the extra features extracted from the facial detail, and our method achieves the state-of-the-art performance.

Face Forgery Detection by 3D Decomposition

Face recognition (FR) systems are easily constrained by complex environmental situations in the wild. To ensure the accuracy of FR systems, face image quality assessment (FIQA) is applied to reject low-quality face image unsuitable for recognition. Face quality can be defined as the accuracy or confidence of face images being correctly recognized by FR systems, which is desired to be consistent with recognition results. However, current FIQA methods show more or less inconsistency with face recognition due to the following four biases, including implicit constraint, quality labels, regression models, and backbone networks. In order to reduce such biases and enhance the consistency between FR and FIQA, this paper proposes a FIAQ method based on Learning to rank (L2R) algorithm and vision Transformer named L2RT-FIQA. L2RT-FIQA consists of three parts: relative quality labels, L2R framework, and vision Transformer backbone. Specifically, we utilize normalized intra-class and inter-class angular distance to generate relative quality labels; we employ L2R model to focus more on the quality order rather than the absolute quality value; we apply unpretrained vision transformer as our backbone to improve generalization and global information learning. Experimental results show our L2RT-FIQA effectively reduces the aforementioned four kinds of biases and outperforms other state-of-the-art FIQA methods on several challenging benchmarks. 

L2RT-FIQA: Face Image Quality Assessment via Learning-to-Rank Transformer

In order to study the detection effect of optical detection on corona discharge in gas insulated switchgear (GIS), a photomultiplier tube (PMT) based optical detection method test platform is built. The needle-plate electrode is used to simulate corona discharge. An oscilloscope is used to collect the optical signal of corona discharge, a high-voltage probe is used to obtain the reference voltage, and the phase resolved partial discharge (PRPD) pattern of the optical signal is analyzed. The results show that with the development of partial discharge (PD), the PRPD pattern can obviously unfold in different stages, which indicates that the optical detection for GIS has high detection sensitivity and good discrimination of the severity of discharge development. By combining the results of the PRPD pattern and previous studies, it is inferred that the negative half cycle discharge of AC corona discharge mainly depends on the electron avalanche ($\alpha$-process) and the secondary electron avalanche on the cathode surface ($\gamma$-process), and the light radiation is mainly excitation radiation and bremsstrahlung. The positive half cycle mainly depends on spatial photoionization (anode streamer), and the light radiation mainly consists of recombination radiation, excitation radiation and bremsstrahlung. This paper can provide a reference for the application of photoelectric devices used in GIS.

Research on GIS corona discharge PRPD pattern based on optical detection

Face manipulation methods develop rapidly in recent years, which can generate high quality manipulated face images. However, detection methods perform not well on data produced by state-of-the-art manipulation methods, and they lack of generalization ability. In this paper, we propose a novel manipulated face detector, which is based on spatial and frequency domain combination and attention mechanism. Spatial domain features are extracted by facial semantic segmentation, and frequency domain features are extracted by Discrete Fourier Transform. We use features both in spatial domain and frequency domain as inputs in proposed model. And we add attention-based layers to backbone networks, in order to improve its generalization ability. We evaluate proposed model on several datasets and compare it with other state-of-the-art manipulated face detection methods. The results show our model performs best on both seen and unseen data.

Manipulated Face Detector: Joint Spatial and Frequency Domain Attention Network

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L2RT-FIQA: Face Image Quality Assessment via Learning-to-Rank Transformer

Research on GIS corona discharge PRPD pattern based on optical detection

Manipulated Face Detector: Joint Spatial and Frequency Domain Attention Network