An Improved Fingerprinting Algorithm for Detection of Video Frame Duplication Forgery

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.

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A New Algorithm for Image Copy-Move Forgery Detection

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.433-440.5930 ◽

2012 ◽

Vol 433-440 ◽

pp. 5930-5934 ◽

Cited By ~ 1

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.

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

International Journal of Innovative Computing ◽

10.11113/ijic.v9n2.194 ◽

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.

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

International Journal of Innovative Computing ◽

10.11113/ijic.v8n1.152 ◽

2018 ◽

Vol 8 (1) ◽

Cited By ~ 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.

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A JPEG Steganographic Method Based on Syndrome-Trellis Codes with Wetness-Scale

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.263-266.2990 ◽

2012 ◽

Vol 263-266 ◽

pp. 2990-2994

Author(s):

Xue Yi Ye ◽

Guo Peng Lu ◽

Wei Fang Zhou ◽

Yun Lu Wang

Keyword(s):

Low Frequency ◽

Experimental Results ◽

Detection Accuracy ◽

Impact Model ◽

Trellis Codes ◽

Information Embedding ◽

Dct Coefficients ◽

Syndrome Trellis Codes ◽

Embedding Rate

Aiming at the JSTC’s shortcomings, we present an improved JPEG Steganographic method using Syndrome-Trellis Codes with Wetness-Scale in this paper. Firstly, the proposed method excludes the DC coefficients from usable coefficients for information embedding; secondly, when using Syndrome-Trellis Codes to modulate the message bits, it assigns each individual cover element with a special changing cost value provided by our predefined embedding impact model (i.e. wetness-scale), and endeavors to constrain the embedding changes to the low-frequency and middle-frequency DCT coefficients and minimize the embedding impact. The experimental results show that in comparison with JSTC and Jsteg 、F5、MB, the proposed method has stronger ability of anti-steganalysis, and especially when at the embedding rate 0.1, its highest detection accuracy by one of the current best bind steganalysis methods only achieves at 0.5435.

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Nonoverlapping Blocks Based Copy-Move Forgery Detection

Security and Communication Networks ◽

10.1155/2018/1301290 ◽

2018 ◽

Vol 2018 ◽

pp. 1-11 ◽

Cited By ~ 7

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.

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Performance Evaluation of Distance Metric for Copy Move Forgery Detection

Journal of University of Shanghai for Science and Technology ◽

10.51201/jusst/21/08412 ◽

2021 ◽

Vol 23 (08) ◽

pp. 457-461

Author(s):

Sudhakar K ◽

◽

Dr.Subhash Kulkarni ◽

Keyword(s):

Performance Evaluation ◽

Experimental Results ◽

Distance Metrics ◽

Distance Metric ◽

Forgery Detection ◽

Detection Algorithms ◽

Considerable Impact ◽

Copy Move Forgery Detection ◽

Detection Speed

This paper presents the performance evaluation of various distance metric in copy move forger detection algorithms. The choice of distance metric affects the detection speed. The proposed approach is tested over 9 different distance metrics. The experimental results found indicate the choice of distance metric has a considerable impact on forgery detection speed.

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Multi-Sensor Fusion for Aerial Robots in Industrial GNSS-Denied Environments

Applied Sciences ◽

10.3390/app11093921 ◽

2021 ◽

Vol 11 (9) ◽

pp. 3921

Author(s):

Paloma Carrasco ◽

Francisco Cuesta ◽

Rafael Caballero ◽

Francisco J. Perez-Grau ◽

Antidio Viguria

Keyword(s):

Sensor Fusion ◽

Computational Efficiency ◽

Probabilistic Approach ◽

Laser Scanner ◽

Industrial Applications ◽

Experimental Results ◽

Added Value ◽

Aerial Robots ◽

3D Localization ◽

High Level

The use of unmanned aerial robots has increased exponentially in recent years, and the relevance of industrial applications in environments with degraded satellite signals is rising. This article presents a solution for the 3D localization of aerial robots in such environments. In order to truly use these versatile platforms for added-value cases in these scenarios, a high level of reliability is required. Hence, the proposed solution is based on a probabilistic approach that makes use of a 3D laser scanner, radio sensors, a previously built map of the environment and input odometry, to obtain pose estimations that are computed onboard the aerial platform. Experimental results show the feasibility of the approach in terms of accuracy, robustness and computational efficiency.

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Multi-Scale Feature Pyramid Network: A Heavily Occluded Pedestrian Detection Network Based on ResNet

Sensors ◽

10.3390/s21051820 ◽

2021 ◽

Vol 21 (5) ◽

pp. 1820

Author(s):

Xiaotao Shao ◽

Qing Wang ◽

Wei Yang ◽

Yun Chen ◽

Yi Xie ◽

...

Keyword(s):

Semantic Information ◽

Detection System ◽

Pedestrian Detection ◽

Detection Accuracy ◽

The Public ◽

Scale Feature ◽

Detection Algorithms ◽

Multi Scale ◽

Art Works ◽

Feature Pyramid

The existing pedestrian detection algorithms cannot effectively extract features of heavily occluded targets which results in lower detection accuracy. To solve the heavy occlusion in crowds, we propose a multi-scale feature pyramid network based on ResNet (MFPN) to enhance the features of occluded targets and improve the detection accuracy. MFPN includes two modules, namely double feature pyramid network (FPN) integrated with ResNet (DFR) and repulsion loss of minimum (RLM). We propose the double FPN which improves the architecture to further enhance the semantic information and contours of occluded pedestrians, and provide a new way for feature extraction of occluded targets. The features extracted by our network can be more separated and clearer, especially those heavily occluded pedestrians. Repulsion loss is introduced to improve the loss function which can keep predicted boxes away from the ground truths of the unrelated targets. Experiments carried out on the public CrowdHuman dataset, we obtain 90.96% AP which yields the best performance, 5.16% AP gains compared to the FPN-ResNet50 baseline. Compared with the state-of-the-art works, the performance of the pedestrian detection system has been boosted with our method.

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Privacy Leakage in Mobile Sensing: Your Unlock Passwords Can Be Leaked through Wireless Hotspot Functionality

Mobile Information Systems ◽

10.1155/2016/8793025 ◽

2016 ◽

Vol 2016 ◽

pp. 1-14 ◽

Cited By ~ 15

Author(s):

Jie Zhang ◽

Xiaolong Zheng ◽

Zhanyong Tang ◽

Tianzhang Xing ◽

Xiaojiang Chen ◽

...

Keyword(s):

Feature Extraction ◽

Mobile Sensing ◽

Experimental Results ◽

Smart Devices ◽

Detection Accuracy ◽

Phase Information ◽

Current Sensing ◽

Privacy Leakage ◽

Sensing Systems ◽

Additional Hardware

Mobile sensing has become a new style of applications and most of the smart devices are equipped with varieties of sensors or functionalities to enhance sensing capabilities. Current sensing systems concentrate on how to enhance sensing capabilities; however, the sensors or functionalities may lead to the leakage of users’ privacy. In this paper, we present WiPass, a way to leverage the wireless hotspot functionality on the smart devices to snoop the unlock passwords/patterns without the support of additional hardware. The attacker can “see” your unlock passwords/patterns even one meter away. WiPass leverages the impacts of finger motions on the wireless signals during the unlocking period to analyze the passwords/patterns. To practically implement WiPass, we are facing the difficult feature extraction and complex unlock passwords matching, making the analysis of the finger motions challenging. To conquer the challenges, we use DCASW to extract feature and hierarchical DTW to do unlock passwords matching. Besides, the combination of amplitude and phase information is used to accurately recognize the passwords/patterns. We implement a prototype of WiPass and evaluate its performance under various environments. The experimental results show that WiPass achieves the detection accuracy of 85.6% and 74.7% for passwords/patterns detection in LOS and in NLOS scenarios, respectively.

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