With the continuous development of computer hardware equipment and deep learning technology, it is easier for people to swap faces in videos by currently-emerging multimedia tampering tools, such as the most popular deepfake. It would bring a series of new threats of security. Although many forensic researches have focused on this new type of manipulation and achieved high detection accuracy, most of which are based on supervised learning mechanism with requiring a large number of labeled samples for training. In this paper, we first develop a novel unsupervised detection manner for identifying deepfake videos. The main fundamental behind our proposed method is that the face region in the real video is taken by the camera while its counterpart in the deepfake video is usually generated by the computer; the provenance of two videos is totally different. Specifically, our method includes two clustering stages based on Photo-Response Non-Uniformity (PRNU) and noiseprint feature. Firstly, the PRNU fingerprint of each video frame is extracted, which is used to cluster the full-size identical source video (regardless of its real or fake). Secondly, we extract the noiseprint from the face region of the video, which is used to identify (re-cluster for the task of binary classification) the deepfake sample in each cluster. Numerical experiments verify our proposed unsupervised method performs very well on our own dataset and the benchmark FF++ dataset. More importantly, its performance rivals that of the supervised-based state-of-the-art detectors. 

Unsupervised Learning-Based Framework for Deepfake Video Detection

With the development of facial recognition technology, face anti-spoofing as the most important security module of face recognition system becomes more and more important. As a matter of fact, face anti-spoofing is still a challenging task, especially facing various attacks simultaneously. Moreover, most of current detectors mainly focus on binary classification while usually fail to complete the task of fine-grained multiple classification, referring to as replay, print, partial mask, and full mask attacks. To fill the gap, in this context, it is proposed to design the fine-grained detection network for classifying various face spoofing attack modes. First, we propose to establish a Transformer style network structure for feature extraction, where the convolution mapping operation is adopted instead of traditional linear mapping. Specifically, we adopt the self-attention module for extracting long distance feature, and convolution mapping is used to maintain the model's ability to extract local features. Finally, the simple yet effective linear classifier is introduced for fine-grained classification. Moreover, with the help of the VGG based style-transfer network, the well-designed scheme of data augmentation module is proposed for solving the problem of insufficient training samples. In the large-scale experiments, compared with the baseline detectors, our proposed fine-grained classifier with low computation cost performs its superiority for multiple classification. 

FGDNet: Fine-Grained Detection Network Towards Face Anti-Spoofing

The advances in human face recognition (FR) systems have recorded sublime success for automatic and secured authentication in diverse domains. Although the traditional methods have been overshadowed by face recognition counterpart during this progress, computer vision gains rapid traction, and the modern accomplishments address problems with real-world complexity. However, security threats in FR-based systems are a growing concern that offers a new-fangled track to the research community. In particular, recent past has witnessed ample instances of spoofing attacks where imposter breaches security of the system with an artifact of human face to circumvent the sensor module. Therefore, presentation attack detection (PAD) capabilities are instilled in the system for discriminating genuine and fake traits and anticipation of their impact on the overall behavior of the FR-based systems. To scrutinize exhaustively the current state-of-the-art efforts, provide insights, and identify potential research directions on face PAD mechanisms, this systematic study presents a review of face anti-spoofing techniques that use computational approaches. The study includes advancements in face PAD mechanisms ranging from traditional hardware-based solutions to up-to-date handcrafted features or deep learning-based approaches. We also present an analytical overview of face artifacts, performance protocols, and benchmark face anti-spoofing datasets. In addition, we perform analysis of the twelve recent state-of-the-art (SOTA) face PAD techniques on a common platform using identical dataset (i.e., REPLAY-ATTACK) and performance protocols (i.e., HTER and ACA). Our overall analysis investigates that despite prevalent face PAD mechanisms demonstrate potential performance, there exist some crucial issues that requisite a futuristic attention. Our analysis put forward a number of open issues such as; limited generalization to unknown attacks, inadequacy of face datasets for DL-models, training models with new fakes, efficient DL-enabled face PAD with smaller datasets, and limited discrimination of handcrafted features. Furthermore, the COVID-19 pandemic is an additional challenge to the existing face-based recognition systems, and hence to the PAD methods. Our motive is to present a complete reference of studies in this field and orient researchers to promising directions. 

/pdf/a-survey-on-face-presentation-attack-detection-mechanisms-dzps48ij.pdf

A survey on face presentation attack detection mechanisms: hitherto and future perspectives

State-of-the-art defense mechanisms against face attacks achieve near perfect accuracies within one of three attack categories, namely adversarial, digital manipulation, or physical spoofs, however, they fail to generalize well when tested across all three categories Poor generalization can be attributed to learning incoherent attacks jointly To overcome this shortcoming, we propose a unified attack detection framework, namely UniFAD, that can automatically cluster 25 coherent attack types belonging to the three categories Using a multi-task learning framework along with k-means clustering, UniFAD learns joint representations for coherent attacks, while uncorrelated attack types are learned separately Proposed UniFAD outperforms prevailing defense methods and their fusion with an overall TDR = 9473% @ 02% FDR on a large fake face dataset consisting of 341K bona fide images and 448K attack images of 25 types across all 3 categories Proposed method can detect an attack within 3 milliseconds on a Nvidia 2080Ti UniFAD can also identify the attack types and categories with 7581% and 9737% accuracies, respectively

Unified Detection of Digital and Physical Face Attacks.

Recently, the Generative Adversarial Networks (GAN) generated images have been spread over the social networks, which brings the new challenge in the the community of media forensics. Although some reliable forensic tools have advanced the study of detecting GAN generated images, while the detection accuracy cannot be guaranteed when facing the malicious post-processing attacks, especially in the practical social network scenario. Thus, in this context, we propose a novel well-designed deep neural network equipped with handed-crafted features for dealing with this problem. In particular, relying on the Cross-color Spatial Co-occurrence Matrix (CSCM), the discriminative features are extracted after carefully analyzing and selecting the most effective color channels. Next, the fused features are fed into the deep neutral network for training a high-efficient forensic detector. Extensive experimental results empirically verify that in most detection scenarios, our proposed detector performs superiorly to the prior arts, especially in the case of post-processing attacks. Moreover, we also highlight the relevance of the proposed detector over the realistic social network platforms, and its generalization capability in three different scenarios. 

CSC-Net: Cross-color Spatial Co-occurrence Matrix Network for Detecting Synthesized Fake Images

Most existing face authentication systems have limitations when facing the challenge raised by presentation attacks, which probably leads to some dangerous activities when using facial unlocking for smart device, facial access to control system, and face scan payment. Accordingly, as a security guarantee to prevent the face authentication from being attacked, the study of face presentation attack detection is developed in this community. In this work, a face presentation attack detector is designed based on residual color texture representation (RCTR). Existing methods lack of effective data preprocessing, and we propose to adopt DW-filter for obtaining residual image, which can effectively improve the detection efficiency. Subsequently, powerful CM texture descriptor is introduced, which performs better than widely used descriptors such as LBP or LPQ. Additionally, representative texture features are extracted from not only RGB space but also more discriminative color spaces such as HSV, YCbCr, and CIE 1976 L∗a∗b (LAB). Meanwhile, the RCTR is fed into the well-designed classifier. Specifically, we compare and analyze the performance of advanced classifiers, among which an ensemble classifier based on a probabilistic voting decision is our optimal choice. Extensive experimental results empirically verify the proposed face presentation attack detector’s superior performance both in the cases of intradataset and interdataset (mismatched training-testing samples) evaluation.

/pdf/towards-face-presentation-attack-detection-based-on-residual-372hc675qe.pdf

Towards Face Presentation Attack Detection Based on Residual Color Texture Representation

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Towards Face Presentation Attack Detection Based on Residual Color Texture Representation