Image Copy Detection and Evolution Visualisation Using Tree Graphs

Image copy detection is an important problem for several applications such as detecting forgery to enforce copyright protection and intellectual property. One of the important problems following copy detection, however, is the assessment of the type of modifications undergone by an original image to form its copies. In this work, we propose a method for quantifying some of these modifications when multiple copies of the same image are available. We also propose an algorithm to estimate temporal precedence between images (i.e., the order of creation of the copies). Using the estimated relations, a tree graph is then built to visualise the history of evolution of the original image into its copies. Our work is important for ensuring better interpretation of image copies after their detection. It also lays a new ground for enhancing image indexing and search on the Web.

Content-Based Image Copy Detection Using Convolutional Neural Network

With the rapid development of network technology, concerns pertaining to the enhancement of security and protection against violations of digital images have become critical over the past decade. In this paper, an image copy detection scheme based on the Inception convolutional neural network (CNN) model in deep learning is proposed. The image dataset is transferred by a number of image processing manipulations and the feature values in images are automatically extracted for learning and detecting the suspected unauthorized digital images. The experimental results show that the proposed scheme takes on an extraordinary role in the process of detecting duplicated images with rotation, scaling, and other content manipulations. Moreover, the mechanism of detecting duplicate images via a convolutional neural network model with different combinations of original images and manipulated images can improve the accuracy and efficiency of image copy detection compared with existing schemes.

CNN Feature-Based Image Copy Detection with Contextual Hash Embedding

As one of the important techniques for protecting the copyrights of digital images, content-based image copy detection has attracted a lot of attention in the past few decades. The traditional content-based copy detection methods usually extract local hand-crafted features and then quantize these features to visual words by the bag-of-visual-words (BOW) model to build an inverted index file for rapid image matching. Recently, deep learning features, such as the features derived from convolutional neural networks (CNN), have been proven to outperform the hand-crafted features in many applications of computer vision. However, it is not feasible to directly apply the existing global CNN features for copy detection, since they are usually sensitive to partial content-discarded attacks, such as copping and occlusion. Thus, we propose a local CNN feature-based image copy detection method with contextual hash embedding. We first extract the local CNN features from images and then quantize them to visual words to construct an index file. Then, as the BOW quantization process decreases the discriminability of these features to some extent, a contextual hash sequence is captured from a relatively large region surrounding each CNN feature and then is embedded into the index file to improve the feature’s discriminability. Extensive experimental results demonstrate that the proposed method achieves a superior performance compared to the related works in the copy detection task.

Image Copy Detection Based on Local Binary Pattern and SVM Classifier

Due to the availability of a large number of image editing software, it is very easy to find duplicate copies of original images. In such a situation, there is a need to develop a robust technique that can be used for the identification of duplicate copies apart from differentiating it from different images. In this paper, we have proposed an image hashing technique based on uniform Local Binary Pattern (LBP). Here, the input image is initially pre-processed before calculating the Local Binary Pattern (LBP) which is used for image identification. Experiments show that proposed hashing gives excellent performance against the Histogram equalization attack. The Receiver Operating Curve (ROC) indicates that the proposed hashing also performs better in terms of robustness and discrimination. Support Vector Machine (SVM) classifier shows that generated features can easily classify images into a set of similar and different images, and can classify new data with a high level of accuracy.

A Review of Hashing based Image Copy Detection Techniques

Images are considered to be natural carriers of information, and a large number of images are created, exchanged and are made available online. Apart from creating new images, the availability of number of duplicate copies of images is a critical problem. Hashing based image copy detection techniques are a promising alternative to address this problem. In this approach, a hash is constructed by using a set of unique features extracted from the image for identification. This article provides a comprehensive review of the state-of-the-art image hashing techniques. The reviewed techniques are categorized by the mechanism used and compared across a set of functional & performance parameters. The article finally highlights the current issues faced by such systems and possible future directions to motivate further research work.