Logo Image Research Articles

Automated optimization-based image-embedding approach through levels-directions decomposition framework

The present research attempts to address an automated optimization-based image-embedding approach through a levels-directions decomposition framework. The subject behind the present approach is to design colour image watermarking with a focus on contourlet representation, whereas the watermarking intensity is accurately calculated via an optimization algorithm with constraint. In the approach presented here, a number of performance monitors for watermarked and logo images are realized to deal with a new fitness function in the aforementioned optimization algorithm. It is worth noting that the first performance monitoring is organized based on the peak signal-to-noise ratio and the structural similarity, whereas the second one is organized based on the normal correlation and the bit error rate, respectively. There is a scrambling module to represent the logo information in disorder, where a number of attacks are simultaneously applied to the watermarked image in order to adjust the appropriate value for watermarking intensity to realize a robust and efficient solution. The ability of a decision maker system is manually taken into account for choosing the best levels and the corresponding directions regarding the contourlet representation, and the investigated results are considered in a number of well-known colour space models including RGB, YIQ and YCbCr. The key contribution of the present research is made based on the new integration of a set of subsystems employed in colour space models under the embedding and de-embedding processes in the contourlet representation, and the watermarking intensity is acquired through the optimization algorithm with constraint to present the competitive outcomes with respect to state-of-the-art benchmarks. The procedure for extracting the information concerning the logo image from the processed watermarked image under a number of attacks is implemented through the approach proposed, whereas there is no information about the original image and the watermarking intensity to be processed.

Adaptive fusion of color and spatial features for noise-robust retrieval of colored logo and trademark images

Due to their uniqueness and high value commercially, logos/trademarks play a key role in e-business based global marketing. However, existing trademark/logo retrieval techniques and content-based image retrieval methods are mostly designed for generic images, which cannot provide effective retrieval of trademarks/logos. Although color and spatial features have been intensively investigated for logo image retrieval, in most cases they were applied separately. When these are combined in a fused manner, a fixed weighting is normally used between them which cannot reflect the significance of these features in the images. When the image quality is degraded by various reasons such as noise, the reliability of color and spatial features may change in different ways, such that the weights between them should be adapted to such changes. In this paper, adaptive fusion of color and spatial descriptors is proposed for colored logo/trademark image retrieval. First, color quantization and k-means are combined for effective dominant color extraction. For each extracted dominant color, a component-based spatial descriptor is derived for local features. By analyzing the image histogram, an adaptive fusion of these two features is achieved for more effective logo abstraction and more accurate image retrieval. The proposed approach has been tested on a database containing over 2300 logo/trademark images. Experimental results have shown that the proposed methodology yields improved retrieval precision and outperforms three state-of-the-art techniques even with added Gaussian, salt and pepper, and speckle noise.

Onion steganography: a novel layering approach

This paper presents a novel layering approach of steganography which implements deception theory by utilizing three types of multimedia, in order to hide the data in video file. In the proposed technique first, audio stream of video file is analyzed and two features are extracted, active zone and silence zone. These features define method numbers where each method number corresponds to one cipher technique which exists in the literature. After selecting the ciphering method, we apply it on the plaintext. In the second layer, first we convert the ciphered text into arbitrary characters and hide the data using chaotic map by generating logo image. In the third layer, we multiply that image with orthogonal matrix which is selected on the basis of silence zones. In the fourth layer, we hide that logo image using DWT with security key. The security key is generated locally by using logistic map, and its initial condition is set on the basis of selected method number. Lastly we hide resultant image into the audio signal using wavelet transform and regenerate the video file. The proposed system is implemented in MATLAB 7.0. Here, we analyzed the SNR values of the regenerated signal and other statistical results to prove that onion steganography works better than silence interval technique, LSB technique, echo hiding technique, magnitude technique and spectrum technique in many aspects.

Features Fusion for Classification of Logos

In this paper, a logo classification system based on the appearance of logo images is proposed. The proposed classification system makes use of global characteristics of logo images for classification. Color, texture, and shape of a logo wholly describe the global characteristics of logo images. The various combinations of these characteristics are used for classification. The combination contains only with single feature or with fusion of two features or fusion of all three features considered at a time respectively. Further, the system categorizes the logo image into: a logo image with fully text or with fully symbols or containing both symbols and texts. The K-Nearest Neighbour (K-NN) classifier is used for classification. Due to the lack of color logo image dataset in the literature, the same is created consisting 5044 color logo images. Finally, the performance of the classification system is evaluated through accuracy, precision, recall and F-measure computed from the confusion matrix. The experimental results show that the most promising results are obtained for fusion of features.

An Invisible Logo Watermarking Using Arnold Transform

Digital watermarking is the process of hiding information into the digital content. The method of embedding a smaller logo image into the host image is called logo watermarking. The system proposes an invisible and secure watermarking. The key entered initially determine the location of embedding and thus classified the host image to white and black textured regions. The logo image is then transformed using Arnold transform. Discrete Wavelet Transform (DWT) technique is employed for embedding the transformed logo into the white textured regions. Watermark extraction is done by entering the same key which was already entered during embedding. The system is secure and the logo is imperceptible within the host image. Finally for analysis, PSNR value has been used as a metric for determining the quality of the recovered image.

색상 및 형태 특성을 이용한 로고 영상의 기억용이성 측정 및 예측

색상 및 형태 특성을 이용한 로고 영상의 기억용이성 측정 및 예측

A Chaos- Based Video watermarking in Wavelet Domain

A novel video watermarking scheme based on chaotic maps and wavelet transform is proposed in this paper. The algorithm encrypts a logo image as a watermark signal by bit xor’ing the pixel values of logo and a chaotic map, thus the security of the proposed scheme will be increased. In order to enjoy a robust watermarking algorithm, the chaotic watermark signal is embedded into the discrete wavelet transform coefficients of I-frames,. The investigational results demonstrate that the proposed design can achieve outstanding robustness to common attacks, and it surpasses the existing techniques.

Digital Image Watermarking via Adaptive Logo Texturization.

Grayscale logo watermarking is a quite well-developed area of digital image watermarking which seeks to embed into the host image another smaller logo image. The key advantage of such an approach is the ability to visually analyze the extracted logo for rapid visual authentication and other visual tasks. However, logos pose new challenges for invisible watermarking applications which need to keep the watermark imperceptible within the host image while simultaneously maintaining robustness to attacks. This paper presents an algorithm for invisible grayscale logo watermarking that operates via adaptive texturization of the logo. The central idea of our approach is to recast the watermarking task into a texture similarity task. We first separate the host image into sufficiently textured and poorly textured regions. Next, for textured regions, we transform the logo into a visually similar texture via the Arnold transform and one lossless rotation; whereas for poorly textured regions, we use only a lossless rotation. The iteration for the Arnold transform and the angle of lossless rotation are determined by a model of visual texture similarity. Finally, for each region, we embed the transformed logo into that region via a standard wavelet-based embedding scheme. We employ a multistep extraction stage, in which an affine parameter estimation is first performed to compensate for possible geometrical transformations. Testing with multiple logos on a database of host images and under a variety of attacks demonstrates that the proposed algorithm yields better overall performance than competing methods.

Utilisation of website logo for phishing detection

Phishing is a security threat which combines social engineering and website spoofing techniques to deceive users into revealing confidential information. In this paper, we propose a phishing detection method to protect Internet users from the phishing attacks. In particular, given a website, our proposed method will be able to detect if it is a phishing website. We use a logo image to determine the identity consistency between the real and the portrayed identity of a website. Consistent identity indicates a legitimate website and inconsistent identity indicates a phishing website. The proposed method consists of two processes, namely logo extraction and identity verification. The first process will detect and extract the logo image from all the downloaded image resources of a webpage. In order to detect the right logo image, we utilise a machine learning technique. Based on the extracted logo image, the second process will employ the Google image search to retrieve the portrayed identity. Since the relationship between the logo and domain name is exclusive, it is reasonable to treat the domain name as the identity. Hence, a comparison between the domain name returned by Google with the one from the query website will enable us to differentiate a phishing from a legitimate website. The conducted experiments show reliable and promising results. This proves the effectiveness and feasibility of using a graphical element such as a logo to detect a phishing website.

Vehicle Logo Recognition System Based on Convolutional Neural Networks With a Pretraining Strategy

Since a vehicle logo is the clearest indicator of a vehicle manufacturer, most vehicle manufacturer recognition (VMR) methods are based on vehicle logo recognition. Logo recognition can be still a challenge due to difficulties in precisely segmenting the vehicle logo in an image and the requirement for robustness against various imaging situations simultaneously. In this paper, a convolutional neural network (CNN) system has been proposed for VMR that removes the requirement for precise logo detection and segmentation. In addition, an efficient pretraining strategy has been introduced to reduce the high computational cost of kernel training in CNN-based systems to enable improved real-world applications. A data set containing 11 500 logo images belonging to 10 manufacturers, with 10 000 for training and 1500 for testing, is generated and employed to assess the suitability of the proposed system. An average accuracy of 99.07% is obtained, demonstrating the high classification potential and robustness against various poor imaging situations.

Robust and Reversible Image Watermarking Scheme Using Combined DCT-DWT-SVD Transforms

We present a secure and robust image watermarking scheme that uses combined reversible DWT-DCT-SVD transformations to increase integrity, authentication, and confidentiality. The proposed scheme uses two different kinds of watermarking images: a reversible watermark, , which is used for verification (ensuring integrity and authentication aspects); and a second one, , which is defined by a logo image that provides confidentiality. Our proposed scheme is shown to be robust, while its performances are evaluated with respect to the peak signal-to-noise ratio (PSNR), signal-to-noise ratio (SNR), normalized cross-correlation (NCC), and running time. The robustness of the scheme is also evaluated against different attacks, including a compression attack and Salt & Pepper attack.

Automated Color Logo Recognition System based on Shape and Color Features

This paper proposes an automated system for rotation and scale invariant color logo recognition. Colored logo images are recognized using one shape feature namely Moments Invariant and a color feature namely Color Moments. Shape of the logo is modeled using the first two central normalized Hu’s invariant moments while color is modeled using the mean, standard deviation, skewness and kurtosis calculated from the color channels. Each image is scaled and rotated by arbitrary amounts before being submitted to the recognition engine. Classification is done using Manhattan and Euclidean distances. Experimental verification is obtained using a data set of 900 images divided into 75 classes. The proposed approach is highly scalable and robust providing accuracy results comparable to the state of the art. General Terms Image Processing, Pattern Recognition, Color and Shape features.

Logo Recognition Technique using Sift Descriptor, Surf Descriptor and Hog Descriptor

Logos sometimes also known as trademark have high importance in today’s marketing world. Logo or trademark is of high importance because it carries the goodwill of the company and the product. Logo matching and recognition is important to discover either improper or unauthorized use of logos. Query images may come with different types of scale, rotation, affine distortion, illumination noise, highly occluded noise. Sift descriptor, surf descriptor and hog descriptor are very good features to use among the existing techniques to recognize the logo images from such difficulties more accurately. General Terms Logo Recognition, invariant to scale, rotation, invariant to illumination noise, occluded objects .

IJARCCE - Computer and Communication Engineering

2 Abstract: This paper propose an improved hybrid color image watermarking algorithm using SVD, DCT and DWT. In this algorithm, SVs of transformed coefficients of RGB planes of host image using DCT and DWT are modified with that of color logo image at chosen scaling factors. Simulation results illustrate the good imperceptibility and robustness. The present algorithm bears desirable robustness on various attacks including Gaussian noise, salt & pepper noise, compression, median filtering, average filtering, sharpening, rotation, histogram equalization, left and right cropping, top and bottom cropping and motion blurring attacks. Thus this algorithm is used for application of watermarking in image authentication and copyright protection. The performance analysis of this watermarking algorithm is measured by using MSE, PSNR and CF at various scaling factors.

A Secure and Robust Reversible Watermarking Algorithm using Fuzzy Matching Quad Tree Segmentation (F-QTS) Technique for Digital Images

attainment incompetence of extracting the original image is one of the most challenging tasks in digital image watermarking methods. When an image is embedded into another image, this increases more complications. To overcome this problematic scenario, this paper proposes a Fuzzy based Quad Tree Segmentation (F-QTS) method. In this paper, a binary logo image is embedded into a RGB cover image. Due to the embedding data is an image, Quad Tree Segmentation method is applied in both the images in order to allocate the blocks for logo image. This space allocation is empowered by Fuzzy Rules and the binary logo image is embedded into the R-plane of the cover image. Moreover, performances of the proposed watermarking method is evaluated with the various watermarking attacks and presented in terms of PSNR (Peak signal-to-noise ratio).

State Transition Based Embedding in Cepstrum Domain for Audio Copyright Protection

ABSTRACTIn this paper, we propose a new state transition based embedding (STBE) technique for audio watermarking with high fidelity. Furthermore, we propose a new correlation based encoding (CBE) scheme for binary logo image in order to enhance the payload capacity. The result of CBE is also compared with standard run-length encoding (RLE) compression and Huffman schemes. Most of the watermarking algorithms are based on modulating selected transform domain feature of an audio segment in order to embed given watermark bit. In the proposed STBE method instead of modulating feature of each and every segment to embed data, our aim is to retain the default value of this feature for most of the segments. Thus, a high quality of watermarked audio is maintained. Here, the difference between the mean values (Mdiff) of insignificant complex cepstrum transform (CCT) coefficients of down-sampled subsets is selected as a robust feature for embedding. Mdiff values of the frames are changed only when certain conditions are met. Hence, almost 50% of the times, segments are not changed and still STBE can convey watermark information at receiver side. STBE also exhibits a partial restoration feature by which the watermarked audio can be restored partially after extraction of the watermark at detector side. The psychoacoustic model analysis showed that the noise-masking ratio (NMR) of our system is less than −10dB. As amplitude scaling in time domain does not affect selected insignificant CCT coefficients, strong invariance towards amplitude scaling attacks is also proved theoretically. Experimental results reveal that the proposed watermarking scheme maintains high audio quality and are simultaneously robust to general attacks like MP3 compression, amplitude scaling, additive noise, re-quantization, etc.

Identification of Institutional Logo based on Wavelet Features

The main aim of this paper is to recognize the logos of the input document so as to process the document for its classification and analysis, an algorithm is proposed using texture features based on Discrete Wavelet Transform (DWT) and Fast Fourier Transform (FFT) of object occurrence in a new tessellation of logo images and these features are given to the SVM and KNN classifier for recognizing the logos. The proposed algorithm is experimented on a data set of Institutional logos. The experimental results have shown the average recognition accuracy as 67.74% using NN classifier, 79.35% using KNN classifier and 87.09% using SVM classifier. It is an initial attempt towards the classification of documents based on logos. General Terms Document Image Processing, Pattern Recognition

Reversible Combined R-DWT-DCT-SVD Watermarking Schema

This paper presents a secure and robust image watermarking scheme based on the reversible DWT-DCT-SVD transformations to increase both integrity authentication and confidentiality. The proposed approach uses two types of watermarks image: a reversible watermark W1 used for verification (integrity and authentication aspects), and a second watermark W2 (a logo image) to verify the confidentiality. Performances of proposed method are evaluated with respect to the PSNR (Peak Signal to Noise Ratio), SNR (Signal noise to ratio), and NCC (Normalized Correlation) and to the running time. A comparative study is also provided to show robustness of the technique against different attacks including compression attack and Salt & Pepper.

Design and Research of Logistics Product Anti-Counterfeiting Algorithm Based on 3D Computer Vision

In order to solve the problem that how to prevent forgery and vicarious inventory in logistics products, we put forward to providing digital identity for each logistics products, using 3D computer vision technology in the process of logistics, and checking the logistics product anti-counterfeiting identity. According to the requirement of anti fake mark logistics products by security logistics information management system, we put forward the local matching algorithm based on adaptive weight, distinguish the logo image edge, eliminate the brightness difference caused by the result of the image matching and improve the disparity map 3D computer vision, so as to help the system accurate recognition of anti fake label image logistics products. Simulation results show that, the algorithm of logistics product security and vicarious inventory knowledge management system based on digital identity, can effectively identify the logistics of packaging products, without any misreading of the situation in a variety of external conditions can proves the validity and accuracy of the algorithm.

Self-organizing Map (SOM) Based Data Navigation for Identifying Shape Similarities of Graphic Logos

In this paper, we propose a data navigation approach for identifying the shape similarity of graphic logo images using enhanced SOM based visualization methods. Existing SOM based visualization met...