Double watermarking‐based biometric access control for radio frequency identification card

The integration of data privacy and security into radio frequency identification (RFID) technology, particularly into RFID tags, has become one of the most attractive research areas. A crucial challenge in RFID technology research lies in providing an efficient protection for systems against information theft and illegitimate access. This article proposes a secure solution based on an RFID card for physical biometric access‐control applications. This is done by integrating two biometric modalities, namely face and fingerprint which are secured via a double watermarking technique. The suggested approach is ensured by two levels of watermarking. At the first level, the wavelet packet decomposition watermarking algorithm is used to insert features from the fingerprint (minutiae) in the face image of an authorized person. At the second level, the same watermarking algorithm is employed to insert the fingerprint watermark in the face features extracted by Gabor filters from the previously watermarked face image (at the first level). The obtained secured watermarked biometric data are then integrated in a 1‐kB high frequency proximity RFID card. This combination of both RFID technology and the double watermarking technique provides a biometric control access framework. Compared with the state‐of‐the‐art frameworks, the proposed one ensures a good compromise between a reduced computational complexity and a high level of data security while maintaining a small space of storage and a low cost compared to those of the marketed products.     

Tamper detection and self-recovery of biometric images using salient region-based authentication watermarking scheme

Biometric images can be split into regions of salient (ROI) and regions of background (ROB) based on salient region detection. During the process of watermark embedding, salient regions which contain rich-information are less affected by the watermark, therefore can be embedded into more watermarks, and regions of background (ROB) are susceptible to the effect of watermarks, so that they should be embedded lighter or even none in order to preserve the recognition quality of biometric images. In this paper, a novel scheme for tamper detection and self-recovery of biometric images using salient region-based authentication watermarking is proposed. Firstly, we propose a novel multi-level authentication watermarking scheme, which is used to verify the integrity of biometric images. Secondly, biometric data of these biometric images which is used as information watermarks is embedded into themselves. As a result, verification systems can recover the damaged data of original biometric images with hidden information based on tampering detection result. Experimental results and theoretic analysis show that our proposed scheme can detect tampered regions, and recover biometric data while keeping the recognition quality.     

Securing Fingerprint Images Using PSO-Based Wavelet Domain Watermarking

ABSTRACT

Watermarking techniques are used in biometric systems for the purpose of protecting and authenticating biometric data. This paper presents an efficient scheme to protect and authenticate fingerprint images by watermarking with their corresponding facial images in the wavelet domain using Particle Swarm Optimization (PSO). The key idea is to use PSO to find the best discrete wavelet transform (DWT) coefficients where the facial image data can be embedded. The objective function for PSO is based on the fingerprint image quality with respect to the Structural Similarity index (SSIM) and Orientation Certainty Level index (OCL). As a result, embedding the facial image data in the selected coefficients generated by the proposed method not only results in minimum distortion of the host image but also retains the feature set of the original fingerprint to be used in fingerprint recognition. The robustness of the watermark extracted using the proposed technique has been tested against various image processing attacks. This concept of watermarking a biometric image with another biometric image finds application in multimodal biometric authentication for a more secure system of personal recognition at the receiver's end.     

Hiding iris data for authentication of digital images using wavelet theory

This paper introduces an efficient approach to protect the ownership by hiding an iris data into digital image for an authentication purpose. It is based on the theory of wavelets. The idea is to secretly embed biometric data (iris print) in the content of the image identifying the owner. The system is based on an empirical analysis of biometric and watermarking technologies, and it is split into several processes. The first process is based on iris image analysis, which aids the generation of the iris code (watermark); the second and the third processes deal with embedding and detecting a watermark; and the last process deals with the authentication. A new metric that measures the objective quality of the image based on the detected watermark bit is introduced, which does not require the original unmarked image for watermark detection. Simulation results show the effectiveness and efficiency of the proposed approach. The text was submitted by the author in English.     

Biometric template protection with DCT-based watermarking

In this paper we have addressed a solution of two big issues in design of multimodal system: template protection and fusion strategy. A robust biometric watermarking algorithm is proposed for biometric template protection. The fingerprint feature vector and iris features are used as watermark. Proposed DCT-based watermarking technique embeds watermark in low-frequency AC coefficients of selected 8 $\times $ 8 DCT smoother blocks. Blocks are classified based on human visual system. The robustness of the proposed algorithm is compared with the few state-of-art literature when watermarked image is subjected to possible channel attacks. Decision level fusion strategy is used to improve the overall performance of multimodal system. That is achieved by conditionally limiting the threshold of the fingerprint system to a maximum value, obtained by projecting 50 % of the cross over error rate on to the FRR curve of the iris system.     

基于人工神经网络的半脆弱零水印技术

提出了基于人工神经网络的半脆弱零水印技术。首先在宿主图像中随机选择像素点,然后利用神经网络构建所选择像素点与其3×3邻域像素之间的关系,并与二值水印图像进行异或运算得到水印检测密钥,作为所构造的零水印。由于仅从宿主图像中抽取特征构造水印,而没有向图像中嵌入信息,避免了嵌入水印所导致的图像变形。该技术可以用于图像真实性、完整性认证,并可定位篡改发生的位置,且对于JPEG图像压缩具有一定的稳健性。实验结果证明了算法的有效性。     

基于多模生物模板安全的顽健图像隐藏算法

为了解决多模生物认证中生物模板安全传输问题,提出把人脸图像隐藏嵌入到指纹图像中,用于多模生物特征认证,以提高生物体征识别的安全性和准确性。利用指纹图像归一化、中心点检测,获取嵌入区域的几何失真不变域,提出在几何失真不变域基于奇异值分解（SVD）的多重嵌入算法,嵌入相应的人脸图像。检测者通过相关性优化算法,盲提取人脸图像,再结合载体指纹图像进行多模认证。实验结果表明,该算法能够抵抗旋转、缩放、平移等几何失真,也能抵抗压缩、滤波、噪声等攻击,提高了生物模板的传输安全性,指纹与人脸双模生物认证相比于单模认证具有更高的正确识别率。     

基于CRC码的脆弱数字水印算法

提出了一个用于珍贵作品完整性认证的脆弱数字水印算法。其特点是在认证时不需要原始水印,理论和实验结果证实所提算法具有很强的认证能力。     

Weighted matrix based reversible watermarking scheme using color image

High capacity, secure, reversible watermarking scheme using a color image for image authentication and tampered detection is still an important area of research. In this investigation, we have proposed a weighted matrix based reversible watermarking scheme using the color image which provides image authentication and tampered detection. Here, we decomposed the original image into R, G, B color components and partitioned into (3 × 3) pixel blocks then we performed the sum of entry-wise-multiplication operations using a modified weighted matrix to embed the watermark. The watermark embedding locations are stored within an index file to enhance security, increase data hiding capacity, gain good visual quality, achieve reversibility and confirm authenticity. The proposed watermarking scheme not only perform authentication and tampered detection but also improved both data embedding capacity up to 8.00 (bpp) as well as increase visual quality measured by PSNR, 50.03 (dB). Finally, the scheme is compared with other existing state-of-the-art methods and gives a reasonably better performance in terms of visual quality and hiding capacity. Our scheme has been evaluated through various steganographic analysis and observed that the scheme is secure and robust against various attacks.     

Watermarking and authentication of quantum images based on restricted geometric transformations

A secure, keyless, and blind watermarking and authentication strategy for images on quantum computers, WaQI, is proposed based on restricted geometric transformations. In contrast with conventional digital watermarking techniques where geometric transformations on the contents of an image are considered undesirable, the proposed WaQI scheme utilises the restricted variants (of the quantum versions) of these transformations as the main resources of the watermark embedding and authentication circuits. This is accomplished by a careful analysis of the classical content of the image–watermark pair, based on which a bespoke watermark map that translates into the gate sequences of the quantum watermark embedding and authentication circuits is realised. Simulation-based experimental results involving the classical (i.e. conventional or non-quantum) simulation of the input images, watermark signals, and quantum circuits yielded a 25% improvement in terms of overall watermark-embedding capacity and between 7% and 50.7% in terms of the visible quality of the watermarked images in comparison with select digital watermarking methods for various pairs, thus, demonstrating both the feasibility and capabilities of the proposed WaQI scheme when the necessary quantum hardware are realised physically. This scheme, provides the framework for representing two or more quantum data as a single quantum circuit, and opens the door for other applications aimed at quantum data protection.     

Robust blind watermarking approach against the compression for fingerprint image using 2D-DCT

This article presents a recent blind and robust fingerprint image watermarking scheme based on a two-dimensional discrete cosine transform (2D-DCT). The main focus is to compress the fingerprint image watermarked data for the purpose of reducing the volume of storage or sending over the network. The fingerprint features might be affected by the embedded watermark, compression of fingerprint images and the sending across network, thereby leading to various sets of features or watermark data. In order to address this goal in a differential way, the watermark sequence bit two sub-vectors were utilized. The two sub-vectors were achieved by the two-dimensional discrete cosine transform of the host image. Throughout the extraction stage, the essential distinction between the corresponding sub-vectors of the watermarked fingerprint image resulted explicitly in an embedded watermark sequence. The advantage of the proposed method is that it can develop a new simple blind and robust watermarking scheme by 2D-DCT frequency domain on the whole image. Accomplished results relative to other reliable compression schemes showed that the proposed scheme has greater or equivalent robustness to common image processing and geometric attacks, such as cropping, resizing, and rotation. To extract watermark data, the initial fingerprint image was not necessary. The proposed study was tested using 80 fingerprint images from 10 persons, for each from CASIA-FingerprintV5 and FVC2002 fingerprint databases. Eight fingerprint images for each individual were set as the format at which the watermark was embedded in each one.

     

基于多位平面嵌入的二元纯位相滤波器水印

二元纯位相滤波器（BPOF）数字水印是一种用于图像真实性认证的半脆弱水印技术。它由图像离散Fourier变换（DFT）的幅值和相位分别得到幅值位平面和BPOF,并将BPOF作为水印嵌入到幅值位平面中。本文〖BP）〗在简要分析基于单一幅值位平面嵌入BPOF水印的基础上,讨论了使用多个幅值位平面嵌入水印并进行了性能分析。通过仿真实验得到了优化方案：以多个幅值位平面嵌入水印,而从其中的最高位平面中提取水印可以明显提高水印性能。     

A Framework for Robust Watermarking of H.264-Encoded Video With Controllable Detection Performance

As H.264 digital video becomes more prevalent, the need for copyright protection and authentication methods that are appropriate for this standard will emerge. This paper proposes a robust watermarking algorithm for H.264. We employ a human visual model adapted for a 4 times 4 discrete cosine transform block to increase the payload and robustness while limiting visual distortion. A key-dependent algorithm is used to select a subset of the coefficients that have visual watermarking capacity. Furthermore, the watermark is spread over frequencies and within blocks to avoid error pooling. This increases the payload and robustness without noticeably changing the perceptual quality. We embed the watermark in the coded residuals to avoid decompressing the video; however, we detect the watermark from the decoded video sequence in order to make the algorithm robust to intraprediction mode changes. We build a theoretical framework for watermark detection based on a likelihood ratio test. This framework is used to obtain optimal video watermark detection with controllable detection performance. Our simulation results show that we achieve the desired detection performance in Monte Carlo trials. We demonstrate the robustness of our proposed algorithm to several different attacks     

A fast feature selection technique in multi modal biometrics using cloud framework

Biometrics refers to the process that uses biological or physiological traits to identify individuals. The progress seen in technology and security has a vital role to play in Biometric recognition which is a reliable technique to validate individuals and their identity. The biometric identification is generally based on either their physical traits or their behavioural traits. The multimodal biometrics makes use of either two or more of the modalities to improve recognition. There are some popular modalities of biometrics that are palm print, finger vein, iris, face or fingerprint recognition. Another important challenge found with multimodal biometric features is the fusion, which could result in a large set of feature vectors. Most biometric systems currently use a single model for user authentication. In this existing work, a modified method of heuristics that is efficiently used to identify an optimal feature set that is based on a wrapper-based feature selection technique. The proposed method of feature selection uses the Ant Colony Optimization (ACO) and the Particle Swarm Optimization (PSO) are used to feature extraction and classification process utilizes the integration of face, and finger print texture patterns. The set of training images is converted to grayscale. The crossover operator is applied to generate multiple samples for each number of images. The wok proposed here is pre-planned for each weight of each biometric modality, which ensures that even if a biometric modality does not exist at the time of verification, a person can be certified to provide calculated weights the threshold value. The proposed method is demonstrated better result for fast feature selection in bio metric image authentication and also gives high effectiveness security.     

一种基于自适应量化的半脆弱图像水印算法

提出了一种基于自适应量化的半脆弱图像水印嵌入方案，能够在不参考原始载体的情况下对数字图像同时进行版权保护和内容认证．该半脆弱水印嵌入方案具有以下特点：（1）抽取并利用了原数字水印的特征信息（作为辅助水印）；（2）采纳了基于人眼视觉特性的自适应量化策略；（3）数字水印信息的提取不需要原始载体图像；（4）采用整型提升小波变换，克服了小波域水印算法普遍存在的舍入误差问题；（5）能够同时进行版权保护与内容认证，并可确定篡改发生区域．仿真实验表明：该小波域水印嵌入方案不仅对JPEG压缩、叠加噪声、平滑滤波等常规图像处理具有较好鲁棒性。而且能够对替换等恶意图像篡改做出报警反应，同时其误检率与漏检率等关键技术指标均优于现有半脆弱水印嵌入算法．     

一种新的自适应半脆弱水印算法

提出了一种基于图像内容的自适应半脆弱数字水印算法．该算法首先结合梯度分割阈值选取策略, 自适应抽取图像内容特征并作为水印信息；然后利用载体图像邻域特性自适应确定量化步长,并通过量化调制小波系数嵌入数字水印；最后通过比对提取出的水印信息与重新抽取出的图像内容特征,实现对待检测图像的完整性检验和篡改定位．仿真实验证明, 该自适应半脆弱图像水印算法不仅具有较好的篡改检测与定位能力,而且具有较强的抗攻击能力.     

双重身份认证方法的设计与实现

数字水印作为一种新型的信息隐藏技术而成为目前业界研究的热点。该文从数字水印、指纹识别等技术相结合的角度,利用数字水印的隐藏性和指纹识别的唯一性,提出了一种网络身份认证的新模型,实现了用户身份的双莺认证,使得网上商业行为、资源访问等活动有了安全保障。依据此模型,详细描述了基于奇异值分解的数字水印算法,并介绍了,指纹识别的过程。实验表明,该文所提出的数字水印算法在抵抗各种常规数据处理和攻击(如嵌入强度、高斯噪声、JPEG压缩、滤波、裁剪等)方面具有良好的性能。     

一种基于图象特征的小波水印

本文提出了一种用于图象版权鉴定的小波水印方法。该方法在一定的量化标准下利用小波变换提取图象的特征信息，通过混沌序列对其加密生成数字水印。然后，根据HVS的特点和小波系数间的内在联系确定嵌入强度和位置，将水印信息自适应地嵌入到低频部分。该方法保证了鲁棒性，并实现了脱离原图和参考水印的高概率检测。实验结果表明，这是一种行之有效的图象鉴定方法。     

Multi-block dependency based fragile watermarking scheme for fingerprint images protection

For traditional fragile watermarking schemes, isolated-block tamper which will destroy the minutiae of the fingerprint image can hardly be efficiently detected. In this paper, we propose a multi-block dependency based fragile watermarking scheme to overcome this shortcoming. The images are split into image blocks with size of 8 × 8; a 64-bit watermark is generated for each image block, and then equally partitioned into eight parts. Each part of the watermark is embedded into another image block which is selected by the corresponding secret key. Theoretic analysis and experimental results demonstrate that the proposed method not only can detect and localize the isolated-block tamper on fingerprint images with high detection probability and low false detection probability, but also enhances the systematic security obviously.     

Secure data aggregation in wireless sensor networks: A watermark based authentication supportive approach

In-network processing presents a critical challenge for data authentication in wireless sensor networks (WSNs). Current schemes relying on Message Authentication Code (MAC) cannot provide natural support for this operation since even a slight modification to the data invalidates the MAC. Although some recent works propose using privacy homomorphism to support in-network processing, they can only work for some specific query-based aggregation functions, e.g. SUM, average, etc. In this paper, based on digital watermarking, we propose an end-to-end, statistical approach for data authentication that provides inherent support for in-network processing. In this scheme, authentication information is modulated as watermark and superposed on the sensory data at the sensor nodes. The watermarked data can be aggregated by the intermediate nodes without incurring any en route checking. Upon reception of the sensory data, the data sink is able to authenticate the data by validating the watermark, thereby detecting whether the data has been illegitimately altered. In this way, the aggregation–survivable authentication information is only added at the sources and checked by the data sink, without any involvement of intermediate nodes. Furthermore, the simple operation of watermark embedding and complex operation of watermark detection provide a natural solution of function partitioning between the resource limited sensor nodes and the resource abundant data sink. In addition, the watermark can be embedded in both spatial and temporal domains to provide the flexibility between the detection time and detection granularity. The simulation results show that the proposed scheme can successfully authenticate the sensory data with high confidence.