Previous Face Anti-spoofing (FAS) methods face the challenge of generalizing to unseen domains, mainly because most existing FAS datasets are relatively small and lack data diversity. Thanks to the development of face recognition in the past decade, numerous real face images are available publicly, which are however neglected previously by the existing literature. In this paper, we propose an Anomalous cue Guided FAS (AG-FAS) method, which can effectively leverage large-scale additional real faces for improving model generalization via a De-fake Face Generator (DFG). Specifically, by training on a large-scale real face only dataset, the generator obtains the knowledge of what a real face should be like, and thus has the capability of generating a "real" version of any input face image. Consequently, the difference between the input face and the generated "real" face can be treated as cues of attention for the fake feature learning. With the above ideas, an Off-real Attention Network (OA-Net) is proposed which allocates its attention to the spoof region of the input according to the anomalous cue. Extensive experiments on a total of nine public datasets show our method achieves state-of-the-art results under cross-domain evaluations with unseen scenarios and unknown presentation attacks. Besides, we provide theoretical analysis demonstrating the effectiveness of the proposed anomalous cues.

Current Face Anti-spoofing (FAS) models tend to make overly confident predictions even when encountering unfamiliar scenarios or unknown presentation attacks, which leads to serious potential risks. To solve this problem, we propose a Confidence Aware Face Anti-spoofing (CA-FAS) model, which is aware of its capability boundary, thus achieving reliable liveness detection within this boundary. To enable the CA-FAS to “know what it doesn’t know”, we propose to estimate its confidence during the prediction of each sample. Specifically, we build Gaussian distributions for both the live faces and the known attacks. The prediction confidence for each sample is subsequently assessed using the Mahalanobis distance between the sample and the Gaussians for the “known data”. We further introduce the Mahalanobis distance-based triplet mining to optimize the parameters of both the model and the constructed Gaussians as a whole. Extensive experiments show that the proposed CA-FAS can effectively recognize samples with low prediction confidence and thus achieve much more reliable performance than other FAS models by filtering out samples that are beyond its reliable range.

Most existing out-of-distribution (OOD) detection benchmarks classify samples with novel labels as the OOD data. However, some marginal OOD samples actually have close semantic contents to the in-distribution (ID) sample, which makes determining the OOD sample a Sorites Paradox. In this paper, we construct a benchmark named Incremental Shift OOD (IS-OOD) to address the issue, in which we divide the test samples into subsets with different semantic and covariate shift degrees relative to the ID dataset. The data division is achieved through a shift measuring method based on our proposed Language Aligned Image feature Decomposition (LAID). Moreover, we construct a Synthetic Incremental Shift (Syn-IS) dataset that contains high-quality generated images with more diverse covariate contents to complement the IS-OOD benchmark. We evaluate current OOD detection methods on our benchmark and find several important insights: (1) The performance of most OOD detection methods significantly improves as the semantic shift increases; (2) Some methods like GradNorm may have different OOD detection mechanisms as they rely less on semantic shifts to make decisions; (3) Excessive covariate shifts in the image are also likely to be considered as OOD for some methods. Our code and data are released in https://github.com/qqwsad5/IS-OOD.

Improving generalization to unseen scenarios is one of the greatest challenges in Face Anti-spoofing (FAS). Most previous FAS works focus on domain debiasing to eliminate the distribution discrepancy between training and test data. However, a crucial but usually neglected bias factor is the face identity. Generally, the identity distribution varies across the FAS datasets as the participants in these datasets are from different regions, which will lead to serious identity bias in the cross-dataset FAS tasks. In this work, we resort to causal learning and propose Dual Sampling based Causal Intervention (DSCI) for face anti-spoofing, which improves the generalization of the FAS model by eliminating the identity bias. DSCI treats the bias as a confounder and applies the backdoor adjustment through the proposed dual sampling on the face identity and the FAS feature. Specifically, we first sample the data uniformly on the identity distribution that is obtained by a pretrained face recognition model. By feeding the sampled data into a network, we can get an estimated FAS feature distribution and sample the FAS feature on it. Sampling the FAS feature from a complete estimated distribution can include potential counterfactual features in the training, which effectively expands the training data. The dual sampling process helps the model learn the real causality between the FAS feature and the input liveness, allowing the model to perform more stably across various identity distributions. Extensive experiments demonstrate our proposed method outperforms the state-of-the-art methods on both intra- and cross-dataset evaluations.

This paper proposes a method of fabric structure defect detection based on tactile information. Different from traditional visual-based detection methods, the proposed method uses a tactile sensor to attain the information of fabric. The advantage of using tactile information is to avoid different irregular dyeing patterns and reduce the influence of ambient light. Therefore, the proposed method can be more concise and universal, which makes the defect detection system more robust. Experiments are conducted to verify the performance of the developed tactile method. The results demonstrate that the proposed method is much better than the visual method in the detection of structural defects. In addition, we propose a design of a tactile sensing device that can greatly improve the actual detection efficiency of the tactile method. It is showed that the proposed method is efficient enough to meet our requirements and it provides a possible solution for the application of the tactile method in actual fabric production.