Shearlet Based Video Fingerprint for Content-Based Copy Detection

Content-based copy detection (CBCD) is widely used in copyright control for protecting unauthorized use of digital video and its key issue is to extract robust fingerprint against different attacked versions of the same video. In this paper, the “natural parts” (coarse scales) of the Shearlet coefficients are used to generate robust video fingerprints for content-based video copy detection applications. The proposed Shearlet-based video fingerprint (SBVF) is constructed by the Shearlet coefficients in Scale 1 (lowest coarse scale) for revealing the spatial features and Scale 2 (second lowest coarse scale) for revealing the directional features. To achieve spatiotemporal natural, the proposed SBVF is applied to Temporal Informative Representative Image (TIRI) of the video sequences for final fingerprints generation. A TIRI-SBVF based CBCD system is constructed with use of Invert Index File (IIF) hash searching approach for performance evaluation and comparison using TRECVID 2010 dataset. Common attacks are imposed in the queries such as luminance attacks (luminance change, salt and pepper noise, Gaussian noise, text insertion);geometry attacks (letter box and rotation);and temporal attacks (dropping frame, time shifting). The experimental results demonstrate that the proposed TIRI-SBVF fingerprinting algorithm is robust on CBCD applications on most of the attacks. It can achieve an average F1 score of about 0.99, less than 0.01% of false positive rate (FPR) and 97% accuracy of localization.

Metaheuristics with Optimal Deep Transfer Learning Based Copy-Move Forgery Detection Technique

The extensive availability of advanced digital image technologies and image editing tools has simplified the way of manipulating the image content.An effective technique for tampering the identification is the copy-move forgery.Conventional image processing techniques generally search for the patterns linked to the fake content and restrict the usage in massive data classification.Contrast-ingly,deep learning(DL)models have demonstrated significant performance over the other statistical techniques.With this motivation,this paper presents an Optimal Deep Transfer Learning based Copy Move Forgery Detection(ODTL-CMFD)technique.The presented ODTL-CMFD technique aims to derive a DL model for the classification of target images into the original and the forged/tampered,and then localize the copy moved regions.To perform the feature extraction process,the political optimizer(PO)with Mobile Networks(MobileNet)model has been derived for generating a set of useful vectors.Finally,an enhanced bird swarm algorithm(EBSA)with least square support vector machine(LS-SVM)model has been employed for classifying the digital images into the original or the forged ones.The utilization of the EBSA algorithm helps to properly modify the parameters contained in the Multiclass Support Vector Machine(MSVM)technique and thereby enhance the classification performance.For ensuring the enhanced performance of the ODTL-CMFD technique,a series of simulations have been performed against the benchmark MICC-F220,MICC-F2000,and MICC-F600 datasets.The experimental results have demonstrated the improvised performance of the ODTL-CMFD approach over the other techniques in terms of several evaluation measures.

Video Copy Detection Based on Spatiotemporal Fusion Model

Content-based video copy detection is an active research field due to the need for copyright pro- tection and business intellectual property protection. This paper gives a probabilistic spatiotemporal fusion approach for video copy detection. This approach directly estimates the location of the copy segment with a probabilistic graphical model. The spatial and temporal consistency of the video copy is embedded in the local probability function. An effective local descriptor and a two-level descriptor pairing method are used to build a video copy detection system to evaluate the approach. Tests show that it outperforms the popular voting algorithm and the probabilistic fusion framework based on the Hidden Markov Model, improving F-score （F1） by 8%.

PM-DFT:A New Local Invariant Descriptor Towards Image Copy Detection

Currently, global-features-based image copy detection is vulnerable to geometric transformations like cropping, shift, and rotations. To resolve this problem, some algorithms based on local descriptors have been proposed. However, the local descriptors, which were originally designed for object recognition, are not suitable for copy detection because they cause the problems of false positives and ambiguities. Instead of relying on the local gradient statistic as many existing descriptors do, we propose a new invariant local descriptor based on local polar-mapping and discrete Fourier transform. Then based on this descriptor, we propose a new framework of copy detection, in which virtual prior attacks and attack weight are employed for training and selecting only a few robust features. This consequently improves the storage and detection efficiency. In addition, it is worth noting that the feature matching takes the locations and orientations of interest points into consideration, which increases the number of matched regions and improves the recall. Experimental results show that the new descriptor is more robust and distinctive, and the proposed copy detection scheme using this descriptor can substantially enhance the accuracy and recall of copy detection and lower the false positives and ambiguities.

A sparse representation-based approach for video copy detection

Content-based video copy detection becomes an active research field due to requirement of copyright protection, business intelligence, video retrieval, etc. Although it is assumed in the existing methods that reference database consists of original videos, these videos are difficult to be obtained in many practical cases. In this paper, a copy detection method based on sparse repre- sentation is proposed to make use of some imperfect prototypes of original videos maintained in the reference database. A query video is represented as a linear combination of all the videos in the database. Then we can determine that whether the query has sibling videos in the database based on distribution of coefficients and find them out based on reconstruction error. The experiments show that even with very limited dimensional feature, this method can achieve high performance.

Copy-Move Forgery Verification in Images Using Local Feature Extractors and Optimized Classifiers

Passive image forgery detection methods that identify forgeries without prior knowledge have become a key research focus.In copy-move forgery,the assailant intends to hide a portion of an image by pasting other portions of the same image.The detection of such manipulations in images has great demand in legal evidence,forensic investigation,and many other fields.The paper aims to present copy-move forgery detection algorithms with the help of advanced feature descriptors,such as local ternary pattern,local phase quantization,local Gabor binary pattern histogram sequence,Weber local descriptor,and local monotonic pattern,and classifiers such as optimized support vector machine and optimized NBC.The proposed algorithms can classify an image efficiently as either copy-move forged or authenticated,even if the test image is subjected to attacks such as JPEG compression,scaling,rotation,and brightness variation.CoMoFoD,CASIA,and MICC datasets and a combination of CoMoFoD and CASIA datasets images are used to quantify the performance of the proposed algorithms.The proposed algorithms are more efficient than state-of-the-art algorithms even though the suspected image is post-processed.