Toqeer Mahmood

Content-based image retrieval and semantic automatic image annotation based on the weighted average of triangular histograms using support vector machine

In recent years, the rapid growth of multimedia content makes content-based image retrieval (CBIR) a challenging research problem. The content-based attributes of the image are associated with the position of objects and regions within the image. The addition of image content-based attributes to image retrieval enhances its performance. In the last few years, the bag-of-visual-words (BoVW) based image representation model gained attention and significantly improved the efficiency and effectiveness of CBIR. In BoVW-based image representation model, an image is represented as an order-less histogram of visual words by ignoring the spatial attributes. In this paper, we present a novel image representation based on the weighted average of triangular histograms (WATH) of visual words. The proposed approach adds the image spatial contents to the inverted index of the BoVW model, reduces overfitting problem on larger sizes of the dictionary and semantic gap issues between high-level image semantic and low-level image features. The qualitative and quantitative analysis conducted on three image benchmarks demonstrates the effectiveness of the proposed approach based on WATH.

Copy-Move Forgery Detection Technique for Forensic Analysis in Digital Images

Due to the powerful image editing tools images are open to several manipulations; therefore, their authenticity is becoming questionable especially when images have influential power, for example, in a court of law, news reports, and insurance claims. Image forensic techniques determine the integrity of images by applying various high-tech mechanisms developed in the literature. In this paper, the images are analyzed for a particular type of forgery where a region of an image is copied and pasted onto the same image to create a duplication or to conceal some existing objects. To detect the copy-move forgery attack, images are first divided into overlapping square blocks and DCT components are adopted as the block representations. Due to the high dimensional nature of the feature space, Gaussian RBF kernel PCA is applied to achieve the reduced dimensional feature vector representation that also improved the efficiency during the feature matching. Extensive experiments are performed to evaluate the proposed method in comparison to state of the art. The experimental results reveal that the proposed technique precisely determines the copy-move forgery even when the images are contaminated with blurring, noise, and compression and can effectively detect multiple copy-move forgeries. Hence, the proposed technique provides a computationally efficient and reliable way of copy-move forgery detection that increases the credibility of images in evidence centered applications.

A survey on block based copy move image forgery detection techniques

In todays modern life, digital images have significant importance because they have become a leading source of information dissemination. However, the availability of image editing tools made it easier to forge the contents of a digital image; making the authenticity untrustful. Different techniques can be used to forge the digital images. Copy move forgery is the most popular and common approach where a specific part of an image is copied and pasted elsewhere in the same image to conceal unwanted part or object. In this study, we attempted to survey several passive block based copy move forgery detection techniques. A passive technique attempts to identify forgery in digital images without any prior information. A comparison between various techniques is also included.

Copy–move forgery detection through stationary wavelets and local binary pattern variance for forensic analysis in digital images

The most common image tampering often for malicious purposes is to copy a region of the same image and paste to hide some other region. As both regions usually have same texture properties, therefore, this artifact is invisible for the viewers, and credibility of the image becomes questionable in proof centered applications. Hence, means are required to validate the integrity of the image and identify the tampered regions. Therefore, this study presents an efficient way of copy-move forgery detection (CMFD) through local binary pattern variance (LBPV) over the low approximation components of the stationary wavelets. CMFD technique presented in this paper is applied over the circular regions to address the possible post processing operations in a better way. The proposed technique is evaluated on CoMoFoD and Kodak lossless true color image (KLTCI) datasets in the presence of translation, flipping, blurring, rotation, scaling, color reduction, brightness change and multiple forged regions in an image. The evaluation reveals the prominence of the proposed technique compared to state of the arts. Consequently, the proposed technique can reliably be applied to detect the modified regions and the benefits can be obtained in journalism, law enforcement, judiciary, and other proof critical domains.

An efficient forensic technique for exposing region duplication forgery in digital images

The internet users share a massive amount of digital images daily. The accessibility of powerful image manipulation tools has made the integrity of image contents questionable. The most popular image tampering is to duplicate a region elsewhere in the same image to replicate or conceal some other region. The duplicated regions have identical color and texture attributes that make this artifact invisible to the human eye. Therefore, efficient techniques are required to verify the credibility of image contents by detecting the regions duplicated in the digital images. This paper proposes an efficient technique for exposing region duplication forgery in digital images. The proposed technique divides the approximation (LL) sub-band of shift invariant stationary wavelet transform into overlapping blocks of w × w (i.e. w = 4, 8) sizes. The distinctive features extracted from the overlapping blocks are utilized to expose the region duplication forgeries in digital images. The experimental results of the proposed technique are compared with state-of-the-art techniques that reveal the prominence, and effectiveness of the proposed technique in terms of precision, recall and F1 score for different block sizes. Therefore, the proposed technique can reliably be applied to identify the counterfeited regions and the benefits of the proposed technique can be achieved in different fields for example crime investigation, news reporting, and judiciary.

An effective content-based image retrieval technique for image visuals representation based on the bag-of-visual-words model

For the last three decades, content-based image retrieval (CBIR) has been an active research area, representing a viable solution for retrieving similar images from an image repository. In this article, we propose a novel CBIR technique based on the visual words fusion of speeded-up robust features (SURF) and fast retina keypoint (FREAK) feature descriptors. SURF is a sparse descriptor whereas FREAK is a dense descriptor. Moreover, SURF is a scale and rotation-invariant descriptor that performs better in the case of repeatability, distinctiveness, and robustness. It is robust to noise, detection errors, geometric, and photometric deformations. It also performs better at low illumination within an image as compared to the FREAK descriptor. In contrast, FREAK is a retina-inspired speedy descriptor that performs better for classification-based problems as compared to the SURF descriptor. Experimental results show that the proposed technique based on the visual words fusion of SURF-FREAK descriptors combines the features of both descriptors and resolves the aforementioned issues. The qualitative and quantitative analysis performed on three image collections, namely Corel-1000, Corel-1500, and Caltech-256, shows that proposed technique based on visual words fusion significantly improved the performance of the CBIR as compared to the feature fusion of both descriptors and state-of-the-art image retrieval techniques.

A Novel Technique Based on Visual Words Fusion Analysis of Sparse Features for Effective Content-Based Image Retrieval

Content-based image retrieval (CBIR) is a mechanism that is used to retrieve similar images from an image collection. In this paper, an effective novel technique is introduced to improve the performance of CBIR on the basis of visual words fusion of scale-invariant feature transform (SIFT) and local intensity order pattern (LIOP) descriptors. SIFT performs better on scale changes and on invariant rotations. However, SIFT does not perform better in the case of low contrast and illumination changes within an image, while LIOP performs better in such circumstances. SIFT performs better even at large rotation and scale changes, while LIOP does not perform well in such circumstances. Moreover, SIFT features are invariant to slight distortion as compared to LIOP. The proposed technique is based on the visual words fusion of SIFT and LIOP descriptors which overcomes the aforementioned issues and significantly improves the performance of CBIR. The experimental results of the proposed technique are compared with another proposed novel features fusion technique based on SIFT-LIOP descriptors as well as with the state-of-the-art CBIR techniques. The qualitative and quantitative analysis carried out on three image collections, namely, Corel-A, Corel-B, and Caltech-256, demonstrate the robustness of the proposed technique based on visual words fusion as compared to features fusion and the state-of-the-art CBIR techniques.

Rouleaux red blood cells splitting in microscopic thin blood smear images via local maxima, circles drawing, and mapping with original RBCs.

Splitting the rouleaux RBCs from single RBCs and its further subdivision is a challenging area in computer‐assisted diagnosis of blood. This phenomenon is applied in complete blood count, anemia, leukemia, and malaria tests. Several automated techniques are reported in the state of art for this task but face either under or over splitting problems. The current research presents a novel approach to split Rouleaux red blood cells (chains of RBCs) precisely, which are frequently observed in the thin blood smear images. Accordingly, this research address the rouleaux splitting problem in a realistic, efficient and automated way by considering the distance transform and local maxima of the rouleaux RBCs. Rouleaux RBCs are splitted by taking their local maxima as the centres to draw circles by mid‐point circle algorithm. The resulting circles are further mapped with single RBC in Rouleaux to preserve its original shape. The results of the proposed approach on standard data set are presented and analyzed statistically by achieving an average recall of 0.059, an average precision of 0.067 and F‐measure 0.063 are achieved through ground truth with visual inspection.

Forensic analysis of copy-move forgery in digital images using the stationary wavelets

In this modern life, the digital images are being considered the main source of information sharing. The digital images are being used in various fields such as medical imaging, news reporting, crime investigation, insurance claims etc. However, in the presence of sophisticated image editing tools, the credibility of digital images is the main concern. The copy-move forgery (CMF) is a most popular forgery attack. In CMF, a region is duplicated elsewhere in the same image for producing a forged image. Thus, for detecting this particular artifact an algorithm is presented that utilizes Stationary Wavelet Transform (SWT). The experimental results demonstrate that the algorithm is capable of detecting duplicated blocks precisely and identify multiple CMF effectively, even when the images are contaminated by blurring and noise. The performance of our algorithm in comparison with other systems proves the efficacy of the system with a noticeable increase in detection ratios.

An improved image steganography technique based on MSB using bit differencing

The rapid development of data communication in modern era demands secure exchange of information. Steganography is an established method for hiding data from an unauthorised access. Steganographic techniques hide secret data in different file formats such as: image, text, audio, and video. Invisibility, payload capacity, and security in terms of PSNR and robustness are the key challenges to steganography. In this paper, a novel image stegnography technique based on most significant bits (MSB) of image pixels is proposed. Bit No. 5 is used to store the secret bits based on the difference of bit No. 5 and 6 of cover image. If the difference of bit No. 5 and 6 is different from secret data bit then the value of bit No. 5 is changed. The results state that the proposed technique ensures significant improvements in signal to noise ratio. Usually, the hackers focus on LSB bits for secret data extraction but the proposed technique utilizes the MSB bits that make it more secure from unauthorized access. Furthermore, the presented technique is not only secure, but computationally efficient as well.