Face anti-spoofing (FAS) is an essential mechanism for safeguarding the integrity of automated face recognition systems. Despite substantial advancements, the generalization of existing approaches to real-world applications remains challenging. This limitation can be attributed to the scarcity and lack of diversity in publicly available FAS datasets, which often leads to overfitting during training or saturation during testing. In terms of quantity, the number of spoof subjects is a critical determinant. Most datasets comprise fewer than 2,000 subjects. With regard to diversity, the majority of datasets consist of spoof samples collected in controlled environments using repetitive, mechanical processes. This data collection methodology results in homogenized samples and a dearth of scenario diversity. To address these shortcomings, we introduce the Wild Face Anti-Spoofing (WFAS) dataset, a large-scale, diverse FAS dataset collected in unconstrained settings. Our dataset encompasses 853,729 images of 321,751 spoof subjects and 529,571 images of 148,169 live subjects, representing a substantial increase in quantity. Moreover, our dataset incorporates spoof data obtained from the internet, spanning a wide array of scenarios and various commercial sensors, including 17 presentation attacks (PAs) that encompass both 2D and 3D forms. This novel data collection strategy markedly enhances FAS data diversity. Leveraging the WFAS dataset and Protocol 1 (Known-Type), we host the Wild Face Anti-Spoofing Challenge at the CVPR2023 workshop. Additionally, we meticulously evaluate representative methods using Protocol 1 and Protocol 2 (Unknown-Type). Through an in-depth examination of the challenge outcomes and benchmark baselines, we provide insightful analyses and propose potential avenues for future research. The dataset is released under Insightface.

pdf/wild-face-anti-spoofing-challenge-2023-benchmark-and-results-zhamjvrm.pdf

Wild Face Anti-Spoofing Challenge 2023: Benchmark and Results

The rapid advancement of deepfake technology has enabled the creation of highly realistic forged face images or videos. While deepfake technology adds entertainment to people’s lives, it also poses a potential threat to social security. Deepfake detection is a crucial technology for identifying forged images. However, existing deep learning-based models for deepfake detection often overlook subtle forged traces. To solve this problem, we propose a Progressive Attention Network (PANet). The PANet incorporates two attention modules, namely the Efficient Multi-Scale Attention Module (EMAM) and the Spatial and Channel Attention Module (SCAM), in a progressive manner. The EMAM focuses on crucial facial regions, such as the eyes, nose, and mouth, rather than the entire face. The SCAM facilitates fine-grained feature extraction. Experimental results demonstrate that the proposed method achieves state-of-the-art results on deepfake detection datasets.

Deepfake Detection via a Progressive Attention Network

Face anti-spoofing (FAS) plays a vital role in securing face recognition systems. In state-of-the-art FAS methods, face depth is determined for every position in a facial image. However, face depth varies at different positions, which leads to low accuracy when predicting face depth. As we observe, spoof faces have depth values that are 0, while live faces have depths that are equal to or greater than 0. As a result, for faces that have a depth greater than 0, if they are estimated as merely a positive value, instead of an accurate value, the prediction of whether they are real or not will not change. Further, if a range of depths are considered as one category, then there are more samples per category for the network training. Based on the above observation, in this paper, we propose to aggregate simple depth values to the same category and perform classification to optimize the FAS network. To evaluate the performance of the proposed approach, we perform extensive experiments on four benchmark databases, respectively, OULU-NPU, SiW, CASIA-FASD, and Replay-Attack. The results demonstrate that the proposed approach outperforms state-of-the-art methods on intra-database testing. Furthermore, our proposed approach shows advanced performance on cross-database testing.

Advanced Face Anti-Spoofing with Depth Segmentation

TED-LaST: Towards Robust Backdoor Defense Against Adaptive Attacks

Deepfakes have gained widespread attention as they may give rise to a series of risks ranging from personal reputation damages to national security breaches. A mainstream approach to generating deepfakes is based on Generative Adversarial Networks (GAN). Various methods have been proposed to detect GAN-generated fake content. However, most of them only target a specific GAN and do not generalize well to other unseen GAN architectures. Moreover, many existing methods show poor performance on deepfakes that are imperceptible to the human eye as they heavily rely on visual artifacts, such as unblinking eyes and asymmetric faces. In this work, we exploit the spectral artifacts left by up-sampling operations that are universally used in GAN architectures for detecting high-fidelity deepfake videos. We first divide video frames into blocks containing the most informative areas, e.g., face, eyes, and mouth areas, and use their spectrum to train a ResNet-based classifier to detect GAN-generated images. Experimental results on public datasets show that our method is effective in detecting high-fidelity deepfakes and generalizes well across GANs with the same or similar up-sampling operations.

Blockwise Spectral Analysis for Deepfake Detection in High-fidelity Videos

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Advanced Face Anti-Spoofing with Depth Segmentation

TED-LaST: Towards Robust Backdoor Defense Against Adaptive Attacks

Blockwise Spectral Analysis for Deepfake Detection in High-fidelity Videos