scholarly journals An Improved Fingerprinting Algorithm for Detection of Video Frame Duplication Forgery

2012 ◽  
Vol 4 (3) ◽  
pp. 20-32 ◽  
Author(s):  
Yongjian Hu ◽  
Chang-Tsun Li ◽  
Yufei Wang ◽  
Bei-bei Liu
Keyword(s):  
Computational Efficiency ◽  
State Of The Art ◽  
Experimental Results ◽  
Video Frame ◽  
Detection Accuracy ◽  
Forgery Detection ◽  
Detection Algorithms ◽  
Dct Coefficients ◽  
Duplication Detection ◽  
Frame Duplication

Frame duplication is a common way of digital video forgeries. State-of-the-art approaches of duplication detection usually suffer from heavy computational load. In this paper, the authors propose a new algorithm to detect duplicated frames based on video sub-sequence fingerprints. The fingerprints employed are extracted from the DCT coefficients of the temporally informative representative images (TIRIs) of the sub-sequences. Compared with other similar algorithms, this study focuses on improving fingerprints representing video sub-sequences and introducing a simple metric for the matching of video sub-sequences. Experimental results show that the proposed algorithm overall outperforms three related duplication forgery detection algorithms in terms of computational efficiency, detection accuracy and robustness against common video operations like compression and brightness change.

scholarly journals An Improved Fingerprinting Algorithm for Detection of Video Frame Duplication Forgery

2013 ◽  
pp. 64-76
Author(s):  
Yongjian Hu ◽  
Chang-Tsun Li ◽  
Yufei Wang ◽  
Bei-bei Liu
Keyword(s):  
Computational Efficiency ◽  
Experimental Results ◽  
Video Frame ◽  
Detection Accuracy ◽  
Forgery Detection ◽  
Brightness Change ◽  
Detection Algorithms ◽  
Dct Coefficients ◽  
Duplication Detection ◽  
Frame Duplication

Frame duplication is a common way of digital video forgeries. State-of-the-art approaches of duplication detection usually suffer from heavy computational load. In this paper, the authors propose a new algorithm to detect duplicated frames based on video sub-sequence fingerprints. The fingerprints employed are extracted from the DCT coefficients of the temporally informative representative images (TIRIs) of the sub-sequences. Compared with other similar algorithms, this study focuses on improving fingerprints representing video sub-sequences and introducing a simple metric for the matching of video sub-sequences. Experimental results show that the proposed algorithm overall outperforms three related duplication forgery detection algorithms in terms of computational efficiency, detection accuracy and robustness against common video operations like compression and brightness change.

scholarly journals Nonoverlapping Blocks Based Copy-Move Forgery Detection

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Yu Sun ◽  
Rongrong Ni ◽  
Yao Zhao
Keyword(s):  
Computational Complexity ◽  
State Of The Art ◽  
The State ◽  
Experimental Results ◽  
Forgery Detection ◽  
Detection Algorithms ◽  
Smooth Part ◽  
Block Based ◽  
Copy Move Forgery Detection ◽  
High Computational Complexity

In order to solve the problem of high computational complexity in block-based methods for copy-move forgery detection, we divide image into texture part and smooth part to deal with them separately. Keypoints are extracted and matched in texture regions. Instead of using all the overlapping blocks, we use nonoverlapping blocks as candidates in smooth regions. Clustering blocks with similar color into a group can be regarded as a preprocessing operation. To avoid mismatching due to misalignment, we update candidate blocks by registration before projecting them into hash space. In this way, we can reduce computational complexity and improve the accuracy of matching at the same time. Experimental results show that the proposed method achieves better performance via comparing with the state-of-the-art copy-move forgery detection algorithms and exhibits robustness against JPEG compression, rotation, and scaling.

Passive method for rescale detection using quadrature mirror filter based higher order statistical features

2016 ◽  
Vol 14 (05) ◽  
pp. 1650033 ◽  
Author(s):  
Gajanan K. Birajdar ◽  
Vijay H. Mankar
Keyword(s):  
State Of The Art ◽  
Entropy Measure ◽  
Image Editing ◽  
Neural Network Classifier ◽  
Digital Cameras ◽  
Forgery Detection ◽  
Quadrature Mirror Filter ◽  
Detection Algorithms ◽  
Artificial Neural Network Classifier ◽  
Text Component

High resolution digital cameras and state-of-the-art image editing software tools has given rise to large amount of manipulated images leaving no traces of being subjected to any manipulation. Passive or blind forgery detection algorithms are used in order to determine its authenticity. In this paper, an algorithm is proposed that blindly detects global rescaling operation using the statistical models computed based on quadrature mirror filter (QMF) decomposition. Fuzzy entropy measure is employed to choose the relevant features and to remove non-important features whereas artificial neural network classifier is used for forgery detection. Experimental results are presented on grayscale and [Formula: see text]-component images of UCID database to prove the validity of the algorithm under different interpolation schemes. Results are provided for the detection of rescaled images with JPEG compression, arbitrary cropping and white Gaussian noise addition. Further, results are shown using USC-SIPI database to prove the robustness of the algorithm against the type of database.

A New Algorithm for Image Copy-Move Forgery Detection

2012 ◽  
Vol 433-440 ◽  
pp. 5930-5934 ◽  
Author(s):  
Dong Mei Hou ◽  
Zheng Yao Bai ◽  
Shu Chun Liu
Keyword(s):  
Image Forensics ◽  
Minimum Variance ◽  
Experimental Results ◽  
Phase Correlation ◽  
Detection Accuracy ◽  
Forgery Detection ◽  
Typical Distribution ◽  
Copy Move Forgery Detection ◽  
Minimum Variance Method ◽  
Application Scope

A new image forensics algorithm based on phase correlation is proposed to detect image copy-move forgery. Phase correlation is computed to obtain the typical distribution of correlation value and then minimum variance method is applied to determine the pulse diagram. The spatial offset between copied portion and pasted portion is estimated according to the pulse position, thus the copy-move region can be quickly located. Experimental results indicate that this method is not only implemented easily, but also achieve an effective and accurate location for small tampered areas. With this method, detection accuracy is guaranteed and application scope of the algorithm is extended simultaneously.

scholarly journals Center Symmetric Local Multilevel Pattern Based Descriptor and Its Application in Image Matching

2016 ◽  
Vol 2016 ◽  
pp. 1-9 ◽  
Author(s):  
Hui Zeng ◽  
Xiuqing Wang ◽  
Yu Gu
Keyword(s):  
Computational Efficiency ◽  
Image Matching ◽  
State Of The Art ◽  
Experimental Results ◽  
Image Region ◽  
Local Intensity ◽  
Region Description ◽  
Local Image

This paper presents an effective local image region description method, called CS-LMP (Center Symmetric Local Multilevel Pattern) descriptor, and its application in image matching. The CS-LMP operator has no exponential computations, so the CS-LMP descriptor can encode the differences of the local intensity values using multiply quantization levels without increasing the dimension of the descriptor. Compared with the binary/ternary pattern based descriptors, the CS-LMP descriptor has better descriptive ability and computational efficiency. Extensive image matching experimental results testified the effectiveness of the proposed CS-LMP descriptor compared with other existing state-of-the-art descriptors.

scholarly journals An Improved Discrete Cosine Transformation Block Based Scheme for Copy-move Image Forgery Detection

2019 ◽  
Vol 9 (2) ◽  
Author(s):  
Abdulraheem Hassanat Oyiza ◽  
Mohd Aizaini Maarof
Keyword(s):  
Detection Algorithm ◽  
Detection Accuracy ◽  
Forgery Detection ◽  
Image Block ◽  
Discrete Cosine Transformation ◽  
Image Forgery ◽  
Robust Detection ◽  
Image Forgery Detection ◽  
Dct Coefficients ◽  
Block Based

Copy-moved forgery is a common method to manipulate images. Several attempts of image forgery have been discovered and involves a region been duplicated and copied and pasted on another region of the same image in other to achieve selfish gain. Generally, there are two classification of copy-move forgery detection technique such as the block-based and key point-based. The block-based division is mostly used and divides image into blocks during the stage of image pre-processing before features are extracted, whereas key-point based technique skips the division of image into blocks and directly extracts different local feature from the image. In this paper, we review various block based and key point approach which has been proposed by various researchers. There is a problem of achieving a balance between improving the detection accuracy and having minimal computational complexity. The proposed technique is based on an improved DCT based copy-move image forgery detection (IDB-CFD), which involves using an octagonal block to reduce the number of features for matching, thereby improving detection accuracy while having minimal complexity. The analysis of this work as compared to previous proposed works which is based on a robust detection algorithm for copy-move image forgery (RDA-CF) and involves using circle block to reduce the number of features, results show that previous work represents about 79% of the quantized DCT coefficients on each image block and this proposed work represents about 85% of quantized DCT coefficients, therefore, recovery of about 6% more features using the IDB-CFD technique was observed as the improvement over the previously proposed RDA-CF.

scholarly journals A Novel Active Anomaly Discovery Method and its Applications in Additive Manufacturing

2021 ◽  
Author(s):  
Bo Shen ◽  
Zhenyu Kong
Keyword(s):  
Additive Manufacturing ◽  
Anomaly Detection ◽  
State Of The Art ◽  
Detection Algorithm ◽  
The State ◽  
Detection Accuracy ◽  
Detection Algorithms ◽  
Detection Analysis ◽  
Unsupervised Anomaly Detection ◽  
Discovery Method

Anomaly detection aims to identify the true anomalies from a given set of data instances. Unsupervised anomaly detection algorithms are applied to an unlabeled dataset by producing a ranked list based on anomaly scores. Unfortunately, due to the inherent limitations, many of the top-ranked instances by unsupervised algorithms are not anomalies or not interesting from an application perspective, which leads to high false-positive rates. Active anomaly discovery (AAD) is proposed to overcome this deficiency, which sequentially selects instances to get the labeling information and incorporate it into the anomaly detection algorithm to improve the detection accuracy iteratively. However, labeling is often costly. Therefore, the way to balance detection accuracy and labeling cost is essential. Along this line, this paper proposes a novel AAD method to achieve the goal. Our approach is based on the state-of-the-art unsupervised anomaly detection algorithm, namely, Isolation Forest, to extract features. Thereafter, the sparsity of the extracted features is utilized to improve its anomaly detection performance. To enforce the sparsity of the features and subsequent improvement of the detection analysis, a new algorithm based on online gradient descent, namely, Sparse Approximated Linear Anomaly Discovery (SALAD), is proposed with its theoretical Regret analysis. Extensive experiments on both open-source and additive manufacturing datasets demonstrate that the proposed algorithm significantly outperforms the state-of-the-art algorithms for anomaly detection.

scholarly journals An Improved DCT Block Based Technique for Copy-Move Forgery Detection in Medical Images

2018 ◽  
Vol 8 (1) ◽  
Author(s):  
Abdulraheem Hassanat Oyiza ◽  
Mohd Aizaini Maarof
Keyword(s):  
Dimensionality Reduction ◽  
Detection Technique ◽  
Common Method ◽  
Detection Accuracy ◽  
Forgery Detection ◽  
Image Block ◽  
Dct Coefficients ◽  
Block Based ◽  
Copy Move Forgery Detection

Copy-Moved forgery is a common method to manipulate images. Several attempts of image forgery have been discovered and involves a region been duplicated and copied and pasted on another region of the same image in other to achieve selfish gain. Generally, there are two classification of copy-move forgery detection technique such as the block-based and key point-based. The most commonly used technique is the block based which divides image into blocks during the stage of image pre-processing before features are extracted whereas key point based technique skips the division of image into blocks and directly extracts different local feature from the image. In this paper, we review various block based and key point approach which has been proposed by various researchers. The proposed technique is based on DCT and an improvement on DCT technique is achieved in terms of dimensionality reduction using an octagonal block to reduce the number of features for matching, thereby improving detection accuracy. Based on the analysis of this work as compared to previous proposed works, since previous work represents about 79% of the quantized DCT coefficients on each image block and this proposed work represents about 85% of quantized DCT coefficients, therefore, recovery of about 6% more features using the octagonal block was observed as the improvement over the previously proposed dimensionality reduction using the circle block.

scholarly journals All-in-One Framework for Detection, Unpacking, and Verification for Malware Analysis

2019 ◽  
Vol 2019 ◽  
pp. 1-16 ◽  
Author(s):  
Mi-Jung Choi ◽  
Jiwon Bang ◽  
Jongwook Kim ◽  
Hajin Kim ◽  
Yang-Sae Moon
Keyword(s):  
Detection Method ◽  
State Of The Art ◽  
Experimental Results ◽  
Detection Accuracy ◽  
Malware Analysis ◽  
Integrated Framework ◽  
Detection Techniques ◽  
Verification Algorithm ◽  
Extensive Analysis ◽  
Hybrid Detection

Packing is the most common analysis avoidance technique for hiding malware. Also, packing can make it harder for the security researcher to identify the behaviour of malware and increase the analysis time. In order to analyze the packed malware, we need to perform unpacking first to release the packing. In this paper, we focus on unpacking and its related technologies to analyze the packed malware. Through extensive analysis on previous unpacking studies, we pay attention to four important drawbacks: no phase integration, no detection combination, no real-restoration, and no unpacking verification. To resolve these four drawbacks, in this paper, we present an all-in-one structure of the unpacking system that performs packing detection, unpacking (i.e., restoration), and verification phases in an integrated framework. For this, we first greatly increase the packing detection accuracy in the detection phase by combining four existing and new packing detection techniques. We then improve the unpacking phase by using the state-of-the-art static and dynamic unpacking techniques. We also present a verification algorithm evaluating the accuracy of unpacking results. Experimental results show that the proposed all-in-one unpacking system performs all of the three phases well in an integrated framework. In particular, the proposed hybrid detection method is superior to the existing methods, and the system performs unpacking very well up to 100% of restoration accuracy for most of the files except for a few packers.

scholarly journals Flower End-to-End Detection Based on YOLOv4 Using a Mobile Device

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Zhibin Cheng ◽  
Fuquan Zhang
Keyword(s):  
Computer Vision ◽  
Loss Function ◽  
Mobile Device ◽  
Detection Method ◽  
State Of The Art ◽  
Attention Mechanism ◽  
Detection Accuracy ◽  
Detection Algorithms ◽  
End To End ◽  
Detection Speed

In this paper, a novel flower detection application anchor-based method is proposed, which is combined with an attention mechanism to detect the flowers in a smart garden in AIoT more accurately and fast. While many researchers have paid much attention to the flower classification in existing studies, the issue of flower detection has been largely overlooked. The problem we have outlined deals largely with the study of a new design and application of flower detection. Firstly, a new end-to-end flower detection anchor-based method is inserted into the architecture of the network to make it more precious and fast and the loss function and attention mechanism are introduced into our model to suppress unimportant features. Secondly, our flower detection algorithms can be integrated into the mobile device. It is revealed that our flower detection method is very considerable through a series of investigations carried out. The detection accuracy of our method is similar to that of the state-of-the-art, and the detection speed is faster at the same time. It makes a major contribution to flower detection in computer vision.

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