Indirect synthetic attack on thermal face biometric systems via visible-to-thermal spectrum conversion

Mallat, Khawla; Dugelay, Jean‐Luc

doi:10.1109/cvprw53098.2021.00159

Cited by 5 publications

(2 citation statements)

References 20 publications

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“…Examples include the favtGAN approach [13] where thermal faces are generated by modifying a PatchGAN discriminator [27] to learn auxiliary thermal sensor classes from a combination of different datasets. Mallat et al apply a Cascaded Refinement Network (CRN) [39] based on progressively upsampled feature maps [14,40]. Pavez et al generates a set of stylized thermal facial images using the GansNRoses [41] architecture for thermal facial recognition experiments [42], and [15] uses StyleGAN2 to generate random high resolution thermal faces that are not mapped to existing visible faces.…”

Section: Visible-to-thermal (Vt) Image Translationmentioning

confidence: 99%

When Visible-to-Thermal Facial GAN Beats Conditional Diffusion

Ordun¹,

Raff²,

Purushotham³

2023

Preprint

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Thermal facial imagery offers valuable insight into physiological states such as inflammation and stress by detecting emitted radiation in the infrared spectrum, which is unseen in the visible spectra. Telemedicine applications could benefit from thermal imagery, but conventional computers are reliant on RGB cameras and lack thermal sensors. As a result, we propose the Visible-to-Thermal Facial GAN (VTF-GAN) that is specifically designed to generate high-resolution thermal faces by learning both the spatial and frequency domains of facial regions, across spectra. We compare VTF-GAN against several popular GAN baselines and the first conditional Denoising Diffusion Probabilistic Model (DDPM) for VT face translation (VTF-Diff). Results show that VTF-GAN achieves high quality, crisp, and perceptually realistic thermal faces using a combined set of patch, temperature, perceptual, and Fourier Transform losses, compared to all baselines including diffusion.

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Section: Visible-to-thermal (Vt) Image Translationmentioning

confidence: 99%

When Visible-to-Thermal Facial GAN Beats Conditional Diffusion

Ordun¹,

Raff²,

Purushotham³

2023

Preprint

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“…To generate high-fidelity target NIR modality, Liu et al [180] design a novel subspace-based modality regularization in the cross-modal translation framework. Besides generating the NIR images, Mallat and Dugelay [181] propose a visibleto-thermal conversion scheme to synthesize thermal attacks from RGB face images using a cascaded refinement network. Though effectiveness on intra-dataset testings, one main concern of these methods is that the domain shifts and unknown attacks might significantly influence the generated modality's quality, and the fused features would be unreliable using paired noisy modality data.…”

Section: Multi-modal Fusionmentioning

confidence: 99%

Deep Learning for Face Anti-Spoofing: A Survey

Qin

et al. 2022

IEEE Trans. Pattern Anal. Mach. Intell.

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Face anti-spoofing (FAS) has lately attracted increasing attention due to its vital role in securing face recognition systems from presentation attacks (PAs). As more and more realistic PAs with novel types spring up, early-stage FAS methods based on handcrafted features become unreliable due to their limited representation capacity. With the emergence of large-scale academic datasets in the recent decade, deep learning based FAS achieves remarkable performance and dominates this area. However, existing reviews in this field mainly focus on the handcrafted features, which are outdated and uninspiring for the progress of FAS community. In this paper, to stimulate future research, we present the first comprehensive review of recent advances in deep learning based FAS. It covers several novel and insightful components: 1) besides supervision with binary label (e.g., '0' for bonafide vs. '1' for PAs), we also investigate recent methods with pixel-wise supervision (e.g., pseudo depth map); 2) in addition to traditional intra-dataset evaluation, we collect and analyze the latest methods specially designed for domain generalization and open-set FAS; and 3) besides commercial RGB camera, we summarize the deep learning applications under multi-modal (e.g., depth and infrared) or specialized (e.g., light field and flash) sensors. We conclude this survey by emphasizing current open issues and highlighting potential prospects.

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