Spoofing could pose a major threat to Global Positioning System (GPS) navigation, so the GPS users have to gain an in-depth understanding of GPS spoofing. Since spoofing attack can influence position results, spoof compensation is possible through reducing position deviations. In this paper, a novel processing technique is proposed and the wavelet transform is used to eliminate the impact of sp...

While recent face anti-spoofing methods perform well under the intra-domain setups, an effective approach needs to account for much larger appearance variations of images acquired in complex scenes with different sensors robust performance. In this paper, we present adaptive vision transformers (ViT) cross-domain anti-spoofing. Specifically, adopt ViT as a backbone exploit its strength long-ran...

In this paper we perform an in depth study of how data augmentation techniques improve synthetic or spoofed audio detection. Specifically, propose methods to deal with channel variability, different compressions, bandwidths and unseen spoofing attacks. These challenges, have all been shown significantly degrade the performance based systems anti systems. Our results are on ASVspoof 2021 challen...

The endless spoofing interference affects the credibility of navigation system seriously. In order to suppress forward which is more threatening military signals in GNSS, this paper proposes a suppression algorithm based on angle arrival estimation and multi-satellite fusion. On basis successfully suppressing signal, improves accuracy forwarded reduces attenuation carrier noise ratio real satel...

Face anti-spoofing (FAS) plays a vital role in securing face recognition systems. Previous approaches usually learn spoofing features from single perspective, which only universal cues shared by all attack types are explored. However, such single-perspective-based ignore the differences among various attacks and commonness between certain bona fides, thus tending to neglect some non-universal t...

Face anti-spoofing approach based on domain generalization (DG) has drawn growing attention due to its robustness for unseen scenarios. Existing DG methods assume that the label is known. However, in real-world applications, collected dataset always contains mixture domains, where unknown. In this case, most of existing may not work. Further, even if we can obtain as methods, think just a sub-o...