On Generating Cancelable Biometric Template using Reverse of Boolean XOR

• URL: http://arxiv.org/abs/2404.15394v1
• Date: Tue, 23 Apr 2024 17:11:07 GMT
• Title: On Generating Cancelable Biometric Template using Reverse of Boolean XOR
• Abstract summary: Three different methods have been proposed for generation of Cancelable Biometric templates based on Visual Secret Sharing scheme. It is found that among the three proposed method, M3 generates good quality Cancelable templates and gives best performance in terms of quality.
• Score: 13.850377680320204
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Cancelable Biometric is repetitive distortion embedded in original Biometric image for keeping it secure from unauthorized access. In this paper, we have generated Cancelable Biometric templates with Reverse Boolean XOR technique. Three different methods have been proposed for generation of Cancelable Biometric templates based on Visual Secret Sharing scheme. In each method, one Secret image and n-1 Cover images are used as: (M1) One original Biometric image (Secret) with n- 1 randomly chosen Gray Cover images (M2) One original Secret image with n-1 Cover images, which are Randomly Permuted version of the original Secret image (M3) One Secret image with n-1 Cover images, both Secret image and Cover images are Randomly Permuted version of original Biometric image. Experiment works have performed on publicly available ORL Face database and IIT Delhi Iris database. The performance of the proposed methods is compared in terms of Co-relation Coefficient (Cr), Mean Square Error (MSE), Mean Absolute Error (MAE), Structural Similarity (SSIM), Peak Signal to Noise Ratio (PSNR), Number of Pixel Change Rate (NPCR), and Unified Average Changing Intensity (UACI). It is found that among the three proposed method, M3 generates good quality Cancelable templates and gives best performance in terms of quality. M3 is also better in quantitative terms on ORL dataset while M2 and M3 are comparable on IIT Delhi Iris dataset.

Related papers

• Cancelable Biometric Template Protection Based on Multi-Instance Fusion: A Contralateral Iris Approach [0.0]
  This paper proposes a cancelable contralateral iris template protection scheme.<n>It satisfies the three requirements of ISO/IEC 24745: irreversibility, unlinkability, and confidentiality.<n>Experiments on three datasets, CASIA-IrisV4-Interval, CASIA-IrisV2 (two devices), and CASIA-Iris-Thousand, yield EERs of $0.36$%, $4.88$%, $10.80$%, and $3.35$%, respectively.
  arXiv  Detail & Related papers  (2026-07-03T01:46:11Z)
• UniMark: Unified Adaptive Multi-bit Watermarking for Autoregressive Image Generators [1.7507301683087861]
  We propose a training-free, unified watermarking framework for autoregressive image generators.<n>method introduces three core components: textbf Semantic Grouping (ASG), textbfBlockwise Multi-bit.<n>Experiments on three AR models demonstrate that method achieves state-of-the-art performance in image quality (FID), watermark detection accuracy, and multi-bit message extraction.
  arXiv  Detail & Related papers  (2026-04-12T16:22:24Z)
• ViewMask-1-to-3: Multi-View Consistent Image Generation via Multimodal Diffusion Models [70.28556518166037]
  We introduce ViewMask-1-to-3, a pioneering approach to apply discrete diffusion models to multi-view image generation.<n>By unifying language and vision through masked token prediction, our approach enables progressive generation of multiple viewpoints.<n>Our approach ranks first on average across GSO and 3D-FUTURE datasets in terms of PSNR, SSIM, and LPIPS.
  arXiv  Detail & Related papers  (2025-12-16T05:15:07Z)
• SMILENet: Unleashing Extra-Large Capacity Image Steganography via a Synergistic Mosaic InvertibLE Hiding Network [71.11351750072936]
  We propose a novel synergistic framework that achieves 25 image hiding through three key innovations. A network architecture coordinates reversible and non-reversible operations to efficiently exploit information redundancy in both secret and cover images. A unified training strategy that coordinates complementary modules to achieve 3.0x higher capacity than existing methods with superior visual quality.
  arXiv  Detail & Related papers  (2025-03-07T03:31:47Z)
• CBRW: A Novel Approach for Cancelable Biometric Template Generation based on [13.850377680320204]
  Two novel cancelable biometric template generation methods based on Random Walk (CBRW) have been proposed. The performance of the proposed methods is compared with other state-of-the-art methods.
  arXiv  Detail & Related papers  (2024-04-25T16:49:10Z)
• Greedy-DiM: Greedy Algorithms for Unreasonably Effective Face Morphs [2.0795007613453445]
  Diffusion Morphs (DiM) are a recently proposed morphing attack that has achieved state-of-the-art performance for representation-based morphing attacks. We propose a greedy strategy on the iterative sampling process of DiM models which searches for an optimal step guided by an identity-based function. We find that our proposed algorithm is unreasonably effective, fooling all of the tested FR systems with an MMPMR of 100%, outperforming all other morphing algorithms compared.
  arXiv  Detail & Related papers  (2024-04-09T05:21:32Z)
• DIFFNAT: Improving Diffusion Image Quality Using Natural Image Statistics [39.457325373431836]
  We propose a generic "naturalness" preserving loss function, viz., kurtosis concentration (KC) loss. Our motivation stems from the projected kurtosis concentration property of natural images. To retain the "naturalness" of the generated images, we enforce reducing the gap between the highest and lowest kurtosis values.
  arXiv  Detail & Related papers  (2023-11-16T10:28:59Z)
• PRO-Face S: Privacy-preserving Reversible Obfuscation of Face Images via Secure Flow [69.78820726573935]
  We name it PRO-Face S, short for Privacy-preserving Reversible Obfuscation of Face images via Secure flow-based model. In the framework, an Invertible Neural Network (INN) is utilized to process the input image along with its pre-obfuscated form, and generate the privacy protected image that visually approximates to the pre-obfuscated one.
  arXiv  Detail & Related papers  (2023-07-18T10:55:54Z)
• Fast refacing of MR images with a generative neural network lowers re-identification risk and preserves volumetric consistency [5.040145546652933]
  We propose a novel method for anonymised face generation for 3D T1-weighted scans based on a 3D conditional generative adversarial network. The proposed method takes 9 seconds for face generation and is suitable for recovering consistent post-processing results after defacing.
  arXiv  Detail & Related papers  (2023-05-26T13:34:14Z)
• HybridMIM: A Hybrid Masked Image Modeling Framework for 3D Medical Image Segmentation [29.15746532186427]
  HybridMIM is a novel hybrid self-supervised learning method based on masked image modeling for 3D medical image segmentation. We learn the semantic information of medical images at three levels, including:1) partial region prediction to reconstruct key contents of the 3D image, which largely reduces the pre-training time burden. The proposed framework is versatile to support both CNN and transformer as encoder backbones, and also enables to pre-train decoders for image segmentation.
  arXiv  Detail & Related papers  (2023-03-18T04:43:12Z)
• Semantic-aware One-shot Face Re-enactment with Dense Correspondence Estimation [100.60938767993088]
  One-shot face re-enactment is a challenging task due to the identity mismatch between source and driving faces. This paper proposes to use 3D Morphable Model (3DMM) for explicit facial semantic decomposition and identity disentanglement.
  arXiv  Detail & Related papers  (2022-11-23T03:02:34Z)
• SSH: A Self-Supervised Framework for Image Harmonization [97.16345684998788]
  We propose a novel Self-Supervised Harmonization framework (SSH) that can be trained using just "free" natural images without being edited. Our results show that the proposedSSH outperforms previous state-of-the-art methods in terms of reference metrics, visual quality, and subject user study.
  arXiv  Detail & Related papers  (2021-08-15T19:51:33Z)
• Image Inpainting with Edge-guided Learnable Bidirectional Attention Maps [85.67745220834718]
  We present an edge-guided learnable bidirectional attention map (Edge-LBAM) for improving image inpainting of irregular holes. Our Edge-LBAM method contains dual procedures,including structure-aware mask-updating guided by predict edges. Extensive experiments show that our Edge-LBAM is effective in generating coherent image structures and preventing color discrepancy and blurriness.
  arXiv  Detail & Related papers  (2021-04-25T07:25:16Z)
• Learning to Generate Image Source-Agnostic Universal Adversarial Perturbations [65.66102345372758]
  A universal adversarial perturbation (UAP) can simultaneously attack multiple images. The existing UAP generator is underdeveloped when images are drawn from different image sources. We take a novel view of UAP generation as a customized instance of few-shot learning.
  arXiv  Detail & Related papers  (2020-09-29T01:23:20Z)
• Improved Slice-wise Tumour Detection in Brain MRIs by Computing Dissimilarities between Latent Representations [68.8204255655161]
  Anomaly detection for Magnetic Resonance Images (MRIs) can be solved with unsupervised methods. We have proposed a slice-wise semi-supervised method for tumour detection based on the computation of a dissimilarity function in the latent space of a Variational AutoEncoder. We show that by training the models on higher resolution images and by improving the quality of the reconstructions, we obtain results which are comparable with different baselines.
  arXiv  Detail & Related papers  (2020-07-24T14:02:09Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.