Biblio

Technology has developed to a very great extent, and the use of smart systems has introduced an increasing threat to data security and privacy. Most of the applications are built-in unsecured operating systems, and so there is a growing threat to information cloning, forging tampering counterfeiting, etc.. This will lead to an un-compensatory loss for end-users particularly in banking applications and personal data in social media. A robust and effective algorithm based on elliptic curve cryptography combined with Hill cipher has been proposed to mitigate such threats and increase information security. In this method, ciphertext and DCT coefficients of an image, embedded into the base image based on LSB watermarking. The ciphertext is generated based on the Hill Cipher algorithm. Hill Cipher can, however, be easily broken and has weak security and to add complexity, Elliptic curve cryptography (ECC), is combined with Hill cipher. Based on the ECC algorithm, the key is produced, and this key is employed to generate ciphertext through the Hill cipher algorithm. This combination of both steganography and cryptography results in increased authority and ownership of the data for sub-optimal media applications. It is hard to extract the hidden data and the image without the proper key. The performance for hiding text and image into an image data have been analyzed for sub-optimal multimedia applications.

Millimeter wave imaging radar is indispensible for collision avoidance of self-driving system, especially in optically blurred visions. The range points migration (RPM) is one of the most promising imaging algorithms, which provides a number of advantages from synthetic aperture radar (SAR), in terms of accuracy, computational complexity, and potential for multifunctional imaging. The inherent problem in the RPM is that it suffers from lower angular resolution in narrower frequency band even if higher frequency e.g. millimeter wave, signal is exploited. To address this problem, the k-space decomposition based RPM has been developed. This paper focuses on the experimental validation of this method using the X-band or millimeter wave radar system, and demonstrated that our method significantly enhances the reconstruction accuracy in three-dimensional images for the two simple spheres and realistic vehicle targets.

Automatic Image Analysis, Image Classification, Automatic Object Recognition are some of the aspiring research areas in various fields of Engineering. Many Industrial and biological applications demand Image Analysis and Image Classification. Sample images available for classification may be complex, image data may be inadequate or component regions in the image may have poor visibility. With the available information each Digital Image Processing application has to analyze, classify and recognize the objects appropriately. Pre-processing, Image segmentation, feature extraction and classification are the most common steps to follow for Classification of Images. In this study we applied various existing edge detection methods like Robert, Sobel, Prewitt, Canny, Otsu and Laplacian of Guassian to crab images. From the conducted analysis of all edge detection operators, it is observed that Sobel, Prewitt, Robert operators are ideal for enhancement. The paper proposes Enhanced Sobel operator, Enhanced Prewitt operator and Enhanced Robert operator using morphological operations and masking. The novelty of the proposed approach is that it gives thick edges to the crab images and removes spurious edges with help of m-connectivity. Parameters which measure the accuracy of the results are employed to compare the existing edge detection operators with proposed edge detection operators. This approach shows better results than existing edge detection operators.

This work uses adversarial perturbations to enhance deepfake images and fool common deepfake detectors. We created adversarial perturbations using the Fast Gradient Sign Method and the Carlini and Wagner L2 norm attack in both blackbox and whitebox settings. Detectors achieved over 95% accuracy on unperturbed deepfakes, but less than 27% accuracy on perturbed deepfakes. We also explore two improvements to deep-fake detectors: (i) Lipschitz regularization, and (ii) Deep Image Prior (DIP). Lipschitz regularization constrains the gradient of the detector with respect to the input in order to increase robustness to input perturbations. The DIP defense removes perturbations using generative convolutional neural networks in an unsupervised manner. Regularization improved the detection of perturbed deepfakes on average, including a 10% accuracy boost in the blackbox case. The DIP defense achieved 95% accuracy on perturbed deepfakes that fooled the original detector while retaining 98% accuracy in other cases on a 100 image subsample.

The laser security code has been widely used for providing guarantee for ensuring quality of productions and maintaining market circulation order. The laser security code is printed on the surface of the productions, and it may be disturbed by printing method, printing position, package texture and background, which will make the laser security code cannot work normally. The image enhancement algorithm combining with bilateral filter and contrast limited adaptive histogram equalization is provided, which can realize the enhanced display of laser security code in strong interference background. The performance of this algorithm is analyzed and evaluated by experiments, and it can prove that the indexes of this algorithm are better than others.

Super-resolution (SR) of hyperspectral images (HSIs) aims to enhance the spatial/spectral resolution of hyperspectral imagery and the super-resolved results will benefit many remote sensing applications. A generative adversarial network for HSIs super-resolution (HSRGAN) is proposed in this paper. Specifically, HSRGAN constructs spectral and spatial blocks with residual network in generator to effectively learn spectral and spatial features from HSIs. Furthermore, a new loss function which combines the pixel-wise loss and adversarial loss together is designed to guide the generator to recover images approximating the original HSIs and with finer texture details. Quantitative and qualitative results demonstrate that the proposed HSRGAN is superior to the state of the art methods like SRCNN and SRGAN for HSIs spatial SR.

In this work, robust readout of the data (232 English characters) stored in high-security structural colour QR codes, was achieved by using multiple image processing techniques, specifically, histogram equalization and decorrelation stretching. The decoded structural colour QR codes are generic diffractive RGB-pixelated periodic nanocones selectively activated by laser exposure to obtain the particular design of interest. The samples were imaged according to the criteria determined by the diffraction grating equation for the lighting and viewing angles given the red, green, and blue periodicities of the grating. However, illumination variations all through the samples, cross-module and cross-channel interference effects result in acquiring images with dissimilar lighting conditions which cannot be directly retrieved by the decoding script and need significant preprocessing. According to the intensity plots, even if the intensity values are very close (above 200) at some typical regions of the images with different lighting conditions, their inconsistencies (below 100) at the pixels of one representative region may lead to the requirement for using different methods for recovering the data from all red, green, and blue channels. In many cases, a successful data readout could be achieved by downscaling the images to 300-pixel dimensions (along with bilinear interpolation resampling), histogram equalization (HE), linear spatial low-pass mean filtering, and gamma function, each used either independently or with other complementary processes. The majority of images, however, could be fully decoded using decorrelation stretching (DS) either as a standalone or combinational process for obtaining a more distinctive colour definition.

The barcode is an important link between real life and the virtual world nowadays. One of the most common barcodes is QR code, which its appearance, black and white modules, is not visually pleasing. The QR code is applied to product packaging and campaign promotion in the market. There are more and more stores using QR code for transaction payment. If the QR code is altered or illegally duplicated, it will endanger the information security of users. Therefore, the study uses infrared watermarking to embed the infrared QR code information into the explicit QR code to strengthen the anti-counterfeiting features. The explicit graphic QR code is produced by data hiding with error diffusion in this study. With the optical characteristics of K, one of the four printed ink colors CMYK (Cyan, Magenta, Yellow, Black), only K can be rendered in infrared. Hence, we use the infrared watermarking to embed the implicit QR code information into the explicit graphic QR code. General QR code reader may be used to interpret explicit graphic QR code information. As for implicit QR code, it needs the infrared detector to extract its implicit QR code information. If the QR code is illegally copied, it will not show the hidden second QR code under infrared detection. In this study, infrared watermark hidden in the graphic QR code can enhance not only the aesthetics of QR code, but also the anti-counterfeiting feature. It can also be applied to printing related fields, such as security documents, banknotes, etc. in the future.

There is an inevitable trade-off between spatial and spectral resolutions in optical remote sensing images. A number of data fusion techniques of multimodal images with different spatial and spectral characteristics have been developed to generate optical images with both spatial and spectral high resolution. Although some of the techniques take the spectral and spatial blurring process into account, there is no method that attempts to retrieve an optical image with both spatial and spectral high resolution, a spectral blurring filter and a spectral response simultaneously. In this paper, we propose a new framework of spatial resolution enhancement by a fusion of multiple optical images with different characteristics based on tensor decomposition. An optical image with both spatial and spectral high resolution, together with a spatial blurring filter and a spectral response, is generated via canonical polyadic (CP) decomposition of a set of tensors. Experimental results featured that relatively reasonable results were obtained by regularization based on nonnegativity and coupling.

We present a gradient-based attack against SVM-based forensic techniques relying on high-dimensional SPAM features. As opposed to prior work, the attack works directly in the pixel domain even if the relationship between pixel values and SPAM features can not be inverted. The proposed method relies on the estimation of the gradient of the SVM output with respect to pixel values, however it departs from gradient descent methodology due to the necessity of preserving the integer nature of pixels and to reduce the effect of the attack on image quality. A fast algorithm to estimate the gradient is also introduced to reduce the complexity of the attack. We tested the proposed attack against SVM detection of histogram stretching, adaptive histogram equalization and median filtering. In all cases the attack succeeded in inducing a decision error with a very limited distortion, the PSNR between the original and the attacked images ranging from 50 to 70 dBs. The attack is also effective in the case of attacks with Limited Knowledge (LK) when the SVM used by the attacker is trained on a different dataset with respect to that used by the analyst.

This paper proposes a forensic method for identifying whether an image was previously compressed by JPEG and also proposes an improved anti-forensics method to enhance the quality of noise added image. Stamm and Liu's anti-forensics method disable the detection capabilities of various forensics methods proposed in the literature, used for identifying the compressed images. However, it also degrades the quality of the image. First, we analyze the anti-forensics method and then use the decimal histogram of the coefficients to distinguish the never compressed images from the previously compressed; even the compressed image processed anti-forensically. After analyzing the noise distribution in the AF image, we propose a method to remove the Gaussian noise caused by image dithering which in turn enhances the image quality. The paper is organized in the following manner: Section I is the introduction, containing previous literature. Section II briefs Anti-forensic method proposed by Stamm et al. In section III, we have proposed a forensic approach and section IV comprises of improved anti-forensic approach. Section V covers details of experimentation followed by the conclusion.

Keystroke dynamics is a form of behavioral biometrics that can be used for continuous authentication of computer users. Many classifiers have been proposed for the analysis of acquired user patterns and verification of users at computer terminals. The underlying machine learning methods that use Gaussian density estimator for outlier detection typically assume that the digraph patterns in keystroke data are generated from a single Gaussian distribution. In this paper, we relax this assumption by allowing digraphs to fit more than one distribution via the Gaussian Mixture Model (GMM). We have conducted an experiment with a public data set collected in a controlled environment. Out of 30 users with dynamic text, we obtain 0.08% Equal Error Rate (EER) with 2 components by using GMM, while pure Gaussian yields 1.3% EER for the same data set (an improvement of EER by 93.8%). Our results show that GMM can recognize keystroke dynamics more precisely and authenticate users with higher confidence level.

Wireless Capsule Endoscopy (WCE) is a noninvasive device for detection of gastrointestinal problems especially small bowel diseases, such as polyps which causes gastrointestinal bleeding. The quality of WCE images is very important for diagnosis. In this paper, a new method is proposed to improve the quality of WCE images. In our proposed method for improving the quality of WCE images, Removing Noise and Contrast Enhancement (RNCE) algorithm is used. The algorithm have been implemented and tested on some real images. Quality metrics used for performance evaluation of the proposed method is Structural Similarity Index Measure (SSIM), Peak Signal-to-Noise Ratio (PSNR) and Edge Strength Similarity for Image (ESSIM). The results obtained from SSIM, PSNR and ESSIM indicate that the implemented RNCE method improve the quality of WCE images significantly.

Optical Coherence Tomography (OCT) has shown a great potential as a complementary imaging tool in the diagnosis of skin diseases. Speckle noise is the most prominent artifact present in OCT images and could limit the interpretation and detection capabilities. In this work we evaluate various denoising filters with high edge-preserving potential for the reduction of speckle noise in 256 dermatological OCT B-scans. Our results show that the Enhanced Sigma Filter and the Block Matching 3-D (BM3D) as 2D denoising filters and the Wavelet Multiframe algorithm considering adjacent B-scans achieved the best results in terms of the enhancement quality metrics used. Our results suggest that a combination of 2D filtering followed by a wavelet based compounding algorithm may significantly reduce speckle, increasing signal-to-noise and contrast-to-noise ratios, without the need of extra acquisitions of the same frame.

A number of blind Image Quality Evaluation Metrics (IQEMs) for Unmanned Aerial Vehicle (UAV) photograph application are presented. Nowadays, the visible light camera is widely used for UAV photograph application because of its vivid imaging effect; however, the outdoor environment light will produce great negative influences on its imaging output unfortunately. In this paper, to conquer this problem above, we design and reuse a series of blind IQEMs to analyze the imaging quality of UAV application. The Human Visual System (HVS) based IQEMs, including the image brightness level, the image contrast level, the image noise level, the image edge blur level, the image texture intensity level, the image jitter level, and the image flicker level, are all considered in our application. Once these IQEMs are calculated, they can be utilized to provide a computational reference for the following image processing application, such as image understanding and recognition. Some preliminary experiments for image enhancement have proved the correctness and validity of our proposed technique.

This paper addresses the issue of magnetic resonance (MR) Image reconstruction at compressive sampling (or compressed sensing) paradigm followed by its segmentation. To improve image reconstruction problem at low measurement space, weighted linear prediction and random noise injection at unobserved space are done first, followed by spatial domain de-noising through adaptive recursive filtering. Reconstructed image, however, suffers from imprecise and/or missing edges, boundaries, lines, curvatures etc. and residual noise. Curvelet transform is purposely used for removal of noise and edge enhancement through hard thresholding and suppression of approximate sub-bands, respectively. Finally Genetic algorithms (GAs) based clustering is done for segmentation of sharpen MR Image using weighted contribution of variance and entropy values. Extensive simulation results are shown to highlight performance improvement of both image reconstruction and segmentation problems.

This paper proposes an enhanced method for personal authentication based on finger Knuckle Print using Kekre's wavelet transform (KWT). Finger-knuckle-print (FKP) is the inherent skin patterns of the outer surface around the phalangeal joint of one's finger. It is highly discriminable and unique which makes it an emerging promising biometric identifier. Kekre's wavelet transform is constructed from Kekre's transform. The proposed system is evaluated on prepared FKP database that involves all categories of FKP. The total database of 500 samples of FKP. This paper focuses the different image enhancement techniques for the pre-processing of the captured images. The proposed algorithm is examined on 350 training and 150 testing samples of database and shows that the quality of database and pre-processing techniques plays important role to recognize the individual. The experimental result calculate the performance parameters like false acceptance rate (FAR), false rejection rate (FRR), True Acceptance rate (TAR), True rejection rate (TRR). The tested result demonstrated the improvement in EER (Error Equal Rate) which is very much important for authentication. The experimental result using Kekre's algorithm along with image enhancement shows that the finger knuckle recognition rate is better than the conventional method.