Face recognition (FR) systems are widely deployed but often struggle due to unconstrained image-capturing conditions. Face image quality assessment (FIQA), applied before recognition, mitigates these challenges by filtering out unreliable samples. Current leading FIQA methods evaluate image quality based on the characteristics observed within the FR model pipeline. However, they leave out the inherent differences in identity embeddings between high-and low-quality face images. To this end, we propose Gen-FIQA, which utilizes a generative model to probe and amplify this difference. Specifically, we extract the identity embedding from an input image using a pre-trained FR model, and then use it as a conditioning signal to generate several face images of the same identity. This generation process leverages the inherent prior in the generative model to translate the difference in identity embedding space back to pixel space. To quantify these differences, the quality score is computed as the average cosine similarity between embeddings from the original and generated images. To improve computational efficiency, we further distill GenFIQA into a lightweight regression-based variant, GenFIQA(R). Extensive experiments across five benchmark datasets and four FR models demonstrate the superiority of our methods over thirteen state-of-the-art FIQA methods.

Face forgery detection is a challenging problem due to the diversity and rapid iteration of face manipulation methods, especially in detecting unknown forgery types. To address this challenge, we explore the common features shared among various forgery types. We find that even though multiple manipulation methods leave different invisible forgery traces, the fake faces often exhibit a similar overall pattern of discomfort. Such discomfort can serve as a universal clue across multiple forgery types, thereby possessing the potential to achieve strong generalization in face forgery detection. To this end, we utilize Vision-Language Models (VLMs) to simulate the cognitive process from perceiving the image to generating the sense of discomfort and propose a multi-task Forgery-Discomfort Joint Learning (FDJL) framework to leverage VLMs to perceive and identify fake faces by integrating discomfort cues. Specifically, we collect a Facial Discomfort dataset guided by the uncanny valley theory, enabling the model to extract and learn discomfort features. Extensive experiments demonstrate that our method achieves state-of-the-art performance and exhibits the best generalization for unknown forgery types.

With the rapid growth usage of face recognition in people’s daily life, face anti-spoofing becomes increasingly important to avoid malicious attacks. Recent face anti-spoofing models can reach a high classification accuracy on multiple datasets but these models can only tell people "this face is fake" while lacking the explanation to answer "why it is fake". Such a system undermines trustworthiness and causes user confusion, as it denies their requests without providing any explanations. In this paper, we incorporate XAI into face anti-spoofing and propose a new problem termed X-FAS (eXplainable Face Anti-Spoofing) empowering face anti-spoofing models to provide an explanation. We propose SPTD (SPoof Trace Discovery), an X-FAS method which can discover spoof concepts and provide reliable explanations on the basis of discovered concepts. To evaluate the quality of X-FAS methods, we propose an X-FAS benchmark with annotated spoof traces by experts. We analyze SPTD explanations on face anti-spoofing dataset and compare SPTD quantitatively and qualitatively with previous XAI methods on proposed X-FAS benchmark. Experimental results demonstrate SPTD’s ability to generate reliable explanations.

The rapid evolution of deepfake generation technologies necessitates the development of robust face forgery detection algorithms. Recent studies have demonstrated that wavelet analysis can enhance the generalization abilities of forgery detectors. Wavelets effectively capture key facial contours, often slender, fine-grained, and globally distributed, that may conceal subtle forgery artifacts imperceptible in the spatial domain. However, current wavelet-based approaches fail to fully exploit the distinctive properties of wavelet data, resulting in sub-optimal feature extraction and limited performance gains. To address this challenge, we introduce WMamba, a novel wavelet-based feature extractor built upon the Mamba architecture. WMamba maximizes the utility of wavelet information through two key innovations. First, we propose Dynamic Contour Convolution (DCConv), which employs specially crafted deformable kernels to adaptively model slender facial contours. Second, by leveraging the Mamba architecture, our method captures long-range spatial relationships with linear complexity. This efficiency allows for the extraction of fine-grained, globally distributed forgery artifacts from small image patches. Extensive experiments show that WMamba achieves state-of-the-art (SOTA) performance, highlighting its effectiveness in face forgery detection.

We explore and find the fundamental systemdegrading effects when using multiple orbital-angular-momentum (OAM) beams in a THz communications link under atmospheric turbulence in simulation. Unlike optical links with relatively small divergence effects, the crosstalk performance of THz OAM links is dependent on divergence-related parameters, including OAM mode order, frequency, and beam waist. Simulation results show: (i) for the cases with the same ratio of beam diameter to the Fried parameter (D/r0), the signal power increases and the crosstalk (XT) decreases when increasing the divergence-related parameters; and (ii) for the cases with the same atmospheric structure constant Cn2, the signal power decreases and the XT increases when increasing the divergence-related parameters. Moreover, for building a link where OAM +4 is transmitted with the parameters: (i) beam waist of 0.1 m and link distance of 200 m, and (ii) beam waist of 1 m and link distance of 1 km, the XT from neighbouring mode remains less than -15 dB when carrier wave frequency is <; 1 THz and 0.1 THz, respectively. In addition, simulation results also show that: (i) limited aperture size of the system has high influence on the XT performance under both weak and strong turbulence; and (ii) displacement of the system has high influence on the XT performance under no and weak turbulence.

Generation and detection of millimeter-wave carrying orbital angular momentum (OAM) have been of growing interest. In this paper, we evaluate patch antenna arrays with different arrangements as OAM generators and receivers by simulation. We compare beam evolution processes and steering performance for circular and ring-antenna-array based OAM links. Mode purity of the generated OAM +1 beam fluctuates between 10% and 99% for ring antenna arrays with 10 cm diameters while it remains >99% for circular antenna arrays. Compared to a ring-antenna-array based link, the circular-antenna-array based link could have an ~10 dB lower power loss at the distance up to 0.5m. We also show that a 5cm diameter circular antenna array could steer an OAM +1 beam up to 80° with a mode purity degradation of <;1%, while ring antenna arrays have ~10% mode purity degradation. OAM spectrum analysis shows both the lattice shape and the boundary shape of an antenna array could cause power leakage to harmonic OAM orders. Such power leakages would increase as the designed OAM order or the lattice period d increases, while it would decrease as the array diameter D increases.