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Identification of distortions to FBG spectrum using FBG fixed filters

  • Authors: Kahandawa, Gayan , Epaarachchi, Jayantha , Wang, Hao
  • Date: 2011
  • Type: Text , Conference paper
  • Relation: 18th International Conference on Composites Materials, ICCM 2011
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  • Description: Recent advances in fibre optic sensor technologies have provided great opportunities to develop more sophisticated in-situ SHM systems. There have been a large number of research reports on health monitoring of composite structures using Fibre Bragg Grating (FBG) sensors. Distortion of FBG sensors has been successfully used by many researchers to identify damage and to locate damage in composite structures. Observations of the distorted sensor spectrums due to stress concentrations caused by delaminations and cracks, have been using to estimate the damage conditions. The majority of the research works were focused on the investigation of the spectra of a FBG sensor embedded in the vicinity of a damage, in order to detect and identify the damage by relating to the distortion of the FBG sensor spectra. However the cause of the distortion of FBG spectra not only depends on the consequences of accumulated damage but also loading types and the fibre orientation. Embedding FBG's in-between non parallel fibre layers and the application of torque have caused substantial distortions to the FBG spectra. A reference FBG spectra needs to be incorporated to FBG measurements to identify the variations to the FBG spectrum and to distinguish the other effects causing distortions. For this purpose, a fixed FBG based system was developed to measure the reflected FBG spectra in time domain. The fixed FBG method was used to estimate the peak using non distorted FBG spectra previously. Unfortunately there was no work done on the identification of distortions of FBG spectra using fixed FBG sensors. This paper details the research work performed to identify distortions of reflected spectra of an embedded FBG sensors inside a composite laminate. The developed method will provide the flexibility of input FBG time domain data directly to post processing algorithms for decoding and damage identification.

Detecting delamination in a composite structure using an embedded FBG-AE hybrid system

  • Authors: Kahandawa, Gayan , Zohari, Mohd , Epaarachchi, Jayantha , Lau, Alan
  • Date: 2012
  • Type: Text , Conference paper
  • Relation: 7th Australasian Congress on Applied Mechanics, ACAM 2012 p. 745-753
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  • Description: Distorted spectra of Fibre Bragg Gratings (FBG) sensors have been using in most of research on identifying delaminations in composites which is the most common cause of failures of laminated composite structures. However, it has been shown that there are multiple causes which can produce a similar response spectrum of an FBG sensor. As a consequence an integrated additional monitoring system would enhance the prediction power of FBG based monitoring system. In this research paper we introduce a "FBG-AE hybrid system" concept for the detection of delaminations in composite structures which uses same FBG sensor network for monitor damage using two independent responses from the sensors. The proposed system use spectral responses from FBG sensors and extract strain and acoustic emission data for monitoring purpose. The proposed concept has experimentally investigated with convincing results.

Optimized FBG sensor network for efficient detection of a delamination in FRP structures

  • Authors: Kahandawa, Gayan , Epaarachchi, Jayantha , Wang, Hao , Lau, Alan
  • Date: 2012
  • Type: Text , Conference paper
  • Relation: 8th Asian-Australasian Conference on Composite Materials 2012 p. 1443-1448
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  • Description: Delamination is a potential cause of failure of composite components. Due to the hidden nature of propagation, the detection of delaminations in composites is a time consuming and extremely difficult task. A few decades of research have shown the effectiveness of the embedded fibre Bragg grating (FBG) sensors to detect such damage in fibre reinforced polymeric (FRP) structures. However, a number of sensors are required to detect delaminations within a particular region of a composite structure due the limited receptive range of an FBG sensor. The complexity and the cost of manufacturing increases with the number of sensors attached and therefore, estimation of the optimum number of sensors for efficient identification of damage is an equally important factor to investigate. This paper details a study on optimization of the number of sensors used to monitor damage in a critical region of an FRP structure. A detailed finite element analysis (FEA) was used for the investigation. A delamination and several FBG sensors were simulated in FEA. The strain values at simulated FBG sensors were used as an input for the development of an optimization algorithm, using artificial neural network (ANN). The number of FBG sensors was decreased until the prediction of the algorithm was reached within a 0.1% error level. The optimal number of FBGs was taken at 0.1% error level with a minimum number of epoch. Furthermore, the effect of obsolete sensors of an optimized sensor network on prediction of the delamination, was also investigated. Copyright © (2012) Asian-Australasian Association for Composite Materials (AACM).

Prediction of obsolete FBG sensor using ANN for efficient and robust operation of SHM systems

  • Authors: Kahandawa, Gayan , Epaarachchi, Jayantha , Wang, Hao , Lau, Alan
  • Date: 2012
  • Type: Text , Conference paper
  • Relation: 4th Asia-Pacific Workshop on Structural Health Monitoring p. 546-553
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  • Description: Increased use of FRP composites for critical load bearing components and structures in recent years has raised the alarm for urgent need of a comprehensive health mentoring system to alert users about integrity and the health condition of advanced composite structures. A few decades of research and development work on structural health monitoring systems using Fibre Bragg Grating (FBG) sensors have come to an accelerated phase at the moment to address these demands in advanced composite industries. However, there are many unresolved problems with identification of damage status of composite structures using FBG spectra and many engineering challenges for implementation of such FBG based SHM system in real life situations. This paper details a research work that was conducted to address one of the critical problems of FBG network, the procedures for immediate rehabilitation of FBG sensor networks due to obsolete/broken sensors. In this study an artificial neural network (ANN) was developed and successfully deployed to virtually simulate the broken/obsolete sensors in a FBG sensor network. It has been found that the prediction of ANN network was within 0.1% error levels.

Use of FBG sensors in SHM of aerospace structures

  • Authors: Kahandawa, Gayan , Epaarachchi, Jayantha , Wang, Hao
  • Date: 2012
  • Type: Text , Conference paper
  • Relation: Third Asia Pacific Optical Sensors Conference
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  • Description: This paper discusses the use of Fibre Bragg grating sensors (FBG) in structural health monitoring (SHM) of Fibre reinforced polymer (FRP) aerospace structures. The diminutive sensor provided the capability of embedding inside FRP structures in order to monitor vital potential locations for damage. Some practical problems associate with manufacturing process of FRP with embedded FBG sensors, interrelation of distortion to FBG spectra with damage, and interpretation of FBG spectral responses for identifying the damage will be discussed.

An application of near infra-red fibre bragg grating as dynamic sensor in SHM of thin composite laminates

  • Authors: Zohari, Mohd , Kahandawa, Gayan , Epaarachchi, Jayantha , Lau, Alan , Cook, Kevin , Canning, John
  • Date: 2013
  • Type: Text , Conference paper
  • Relation: Structural health monitoring 2013 : a roadmap to intelligent structures : proceedings of the 9th International Workshop on Structural Health Monitoring p. 267-275
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  • Description: Vibration testing is an essential component in Structural Health Monitoring (SHM). It can provide vital information regarding the integrity of critical structure; for instance, it can provide information on progressive failure monitoring of composites structure in the aerospace industry. Over the past decade, there have been many successful researches showing extraordinary ability of Fiber Bragg grating (FBG) sensors as a dynamic sensor. Ability of acquiring both static and dynamic strain measurements, make FBG sensor as a good alternative to replace the conventional vibration sensors. In addition the physical size of FBG sensor provides greater access to embed them in composite structures without affecting to any properties of the composite. However, in most applications to date, people have used only the FBG with wavelength 1550 nm. Moreover, FBG sensors with this wavelength are commonly use in industries such as telecommunications and medical industries. However, there is an option of using near infra-red (NIR) FBG range which comparably cheap in term of total system design. This paper details the use of near infra-red (NIR) FBGs as dynamic sensors; a part of SHM system for the monitoring of the damages in a thin glass fiber composite plates. Results reveal that the NIR FBG range gives good response to an impact and; also to applied high frequency vibrations.

Distortion index for assessment of damage growth in a composite structures using spectral distortion of embedded FBG sensors

  • Authors: Kahandawa, Gayan , Epaarachchi, Jayantha , Canning, John , Lau, Alan
  • Date: 2013
  • Type: Text , Conference paper
  • Relation: 9th International Workshop on Structural Health Monitoring: A Roadmap to Intelligent Structures, IWSHM 2013 p. 175-181
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  • Description: Structural Health Monitoring Systems based on embedded FBG sensors, to identify damage conditions, are largely dependent on the spectral distortion of the sensors. The uneven stress gradient occurring along the grating of FBG sensors, due to damage inside composite structures can be estimated by analyzing significant changes that appear in the FBG response spectra. However, the stochastic nature of the distorted shape of the FBG spectra makes it difficult to interpret and quantify the existing damage at the location of the FBG sensors. This research works on a novel concept of the "Distortion I ndex (DI)" which is defined using distorted spectra of FBG sensors. I t was observed that the DI increases with t he i ncrease in damage size.

Estimation of strain of distorted FBG sensor spectra using a fixed FBGfilter circuit and an artificial neural network

  • Authors: Kahandawa, Gayan , Epaarachchi, Jayantha , Lau, Alan , Canning, John
  • Date: 2013
  • Type: Text , Conference paper
  • Relation: 2013 IEEE 8th International Conference on Intelligent Sensors, Sensor Networks and Information Processing: Sensing the Future, ISSNIP 2013 p. 89-94
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  • Description: Fibre Bragg Grating (FBG) sensors are extremely sensitive to changes of strain, and are therefore an extremely useful candidate for Structural Health Monitoring (SHM) systems of composite structures. Sensitivity of FBGs to strain gradients originating from damage was observed as an indicator of initiation and propagation of damage in composite structures. To date there have been numerous research works done on distorted FBG spectra due to damage accumulation under controlled environments. Unfortunately, a number of related unresolved problems remain in FBG-based SHM systems development, making the present SHM systems unsuitable for real life applications. This paper reveals a novel configuration of FBG sensors to acquire strain reading and an integrated statistical approach to analyse data in real time. The proposed configuration has proven its capability to overcome practical constraints and the engineering challenges associated with FBG-based SHM systems. A fixed filter decoding system and an integrated artificial neural network algorithm for extracting strain from embedded FBG sensor were proposed and experimentally proved. Furthermore, the laboratory level experimental data was used to verify the accuracy of the system and it was found that the error levels were less than 0.3% in strain predictions.

Extraction and processing of real time strain of embedded FBG sensors using a fixed filter FBG circuit and an artificial neural network

  • Authors: Kahandawa, Gayan , Epaarachchi, Jayantha , Wang, Hao , Canning, John , Lau, Alan
  • Date: 2013
  • Type: Text , Journal article
  • Relation: Measurement: Journal of the International Measurement Confederation Vol. 46, no. 10 (2013), p. 4045-4051
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  • Description: Fibre Bragg Grating (FBG) sensors have been used in the development of structural health monitoring (SHM) and damage detection systems for advanced composite structures over several decades. Unfortunately, to date only a handful of appropriate configurations and algorithm sare available for using in SHM systems have been developed. This paper reveals a novel configuration of FBG sensors to acquire strain reading and an integrated statistical approach to analyse data in real time. The proposed configuration has proven its capability to overcome practical constraints and the engineering challenges associated with FBG-based SHM systems. A fixed filter decoding system and an integrated artificial neural network algorithm for extracting strain from embedded FBG sensor were proposed and experimentally proved. Furthermore, the laboratory level experimental data was used to verify the accuracy of the system and it was found that the error levels were less than 0.3% in predictions. The developed SMH system using this technology has been submitted to US patent office and will be available for use of aerospace applications in due course. © 2013 Elsevier Ltd. All rights reserved.

Indexing Damage using distortion of embedded FBG sensor response spectra

  • Authors: Kahandawa, Gayan , Epaarachchi, Jayantha , Lau, Alan
  • Date: 2013
  • Type: Text , Conference paper
  • Relation: Fourth International Conference on Smart Materials and Nanotechnology in Engineering
  • Full Text: false
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  • Description: Structural Health Monitoring Systems based on embedded FBG sensors, to identify damage conditions, are largely dependent on the spectral distortion of the sensors. The uneven stress gradient occurring along the grating of FBG sensors, due to damage inside composite structures can be estimated by analysing significant changes that appear in the FBG response spectra. However, the stochastic nature of the distorted shape of the FBG spectra makes it difficult to interpret and quantify the existing damage at the location of the FBG sensors. There are several indexing methods proposed by researchers. We have previously presented a novel concept of the “Distortion Index (DI)” which is defined using distorted spectra of FBG sensors. It was observed that the DI increases with the increase in damage size. The Distortion Index (DI) is introduced to create a correlation between the damage and the distortion of the response spectra of a FBG sensor. This index provides the ability to generalise the distortion of FBG spectra for a particular structure. The index can be used to quantify the damage in the structure relative to its original condition, which can be the condition of structure during a regulated time, i.e. a month uninterrupted operation or first hours in operation, of a structure can be used as no damage condition. In this paper we discuss the application of distortion index and comparison with available several other indexes.

NIR fibre bragg grating as dynamic sensor : An application of 1D digital wavelet analysis for signal denoising

  • Authors: Zohari, Mohd , Kahandawa, Gayan , Epaarachchi, Jayantha , Lau, Alan , Canning, John , Cook, Kevin
  • Date: 2013
  • Type: Text , Conference paper
  • Relation: Fourth International Conference on Smart Materials and Nanotechnology in Engineering
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  • Description: During the past decade, many successful studies have evidently shown remarkable capability of Fiber Bragg Gratings (FBG) sensor for dynamic sensing. Most of the research works utilized the 1550 nm wavelength range of FBG sensors. However near infra-red (NIR) FBG sensors can offer the lower cost of Structural health Monitoring (SHM) systems which uses cheaper silicon sources and detectors. Unfortunately, the excessive noise levels that experienced in NIR wavelengths have caused the rejection of sensor that operating in this range of wavelengths for SHM systems. However, with the appropriate use of signal processing tools, these noisy signals can be easily ‘cleaned’. Wavelet analysis is one of the powerful signal processing tools nowadays, not only for time-frequency analysis but also for signal denoising. This present study revealed that the NIR FBG range gave good response to impact signals. Furthermore, these ‘noisy’ signals’ response were successfully filtered using one dimensional wavelet analysis.

Investigation of full-scale fibre composite girders for replacement of hardwood timber girders in Australia

  • Authors: Aravinthan, Thiru , Kahandawa, Gayan
  • Date: 2014
  • Type: Text , Conference paper
  • Relation: 7th International Conference on FRP Composites in Civil Engineering, CICE 2014
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  • Description: Timber bridges are significant asset in bridge inventory. However, managing aging timber bridges stock is a major challenge. Most of the timber bridges has already reached its design service life, and needs to be replaced. The limited financial resources to maintain these bridges and the depleting supply of quality hardwood timber have created a need for a cost effective alternative for the replacement of these timber bridges. Fibre composite girders are a viable solution, but manufacturing cost needs to be reduced to make it commercially viable. A major research and development project on fibre composite alternative girders to replace existing hard-wood timber girders was carried out by the Centre of Excellence in Engineered Fibre Composites (CEEFC). This project was in collaboration with the Queensland Department of Transport and Main Roads (DTMR) and two industry partners. This project aims to develop commercially viable fibre composite bridge girders for the Australian market through the use of advanced manufacturing processes. These full-scale girders have undergone rigours testing including proof load testing, fatigue testing and ultimate strength testing to prove its viability as a suitable replacement girder. This paper presents the performance of these girders and discusses important issues encountered in the production of these fibre composite replacement girders. It is concluded that both fibre composite bridge girder concepts developed by the industry partners meet the set design criteria.

Integrated FBG sensor responses and full field thermo-electric stress approach to monitor damage accumulation in glass fibre reinforced composite plate

  • Authors: Kakei, Ayad , Epaarachchi, Jayantha , Rajic, Nick , Leng, Jinsong , Islam, Mainal , Kahandawa, Gayan
  • Date: 2015
  • Type: Text , Conference proceedings
  • Relation: 10th International Workshop on Structural Health Monitoring 2015: System Reliability for Verification and Implementation, Standford, United States, 1-15th September, published in Proceedings of the 10th International Workshop on Structural Health Monitoring, IWSHM 2015 p. 641-648
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  • Description: Monitoring internal damage status of advanced composite components with distributed sensor network has shown significant success in recent research works. However, application of such a system in a full scale structure is a critically challenging task and maintaining such a system during life time operations is an extremely difficult. An additional non-contactable full field strain measurement system being used to measure outer surface strain field of a composite sample while an embedded FBG sensor closer to an internal void being used to monitor localized strain variation. Recent developments in miniature low-cost microbolometer technology have paved the way to use full field thermo-elastic stress mapping using relatively inexpensive Infra-Red cameras. This paper details a comparison of strain measurements observed from FBG sensors embedded in a composite plate sample at a closer location to a void and full field thermo-elastic stress map. The test coupons were fabricated with a purposely created delamination and sample was loaded by quasi-static and low cycle fatigue uni-axial loads. The FBG responses and IR images were recorded in frequent intervals in order to track the delamination growth. Further the strain variations were studied using a detailed FEA and compared with experimental strain and full field Thermo-elastic stress map. Copyright © 2015 by DEStech Publica tions, Inc.

New artificial intelligence based tire size identification for fast and safe inflating cycle

  • Authors: Kahandawa, Gayan , Choudhury, Tanveer , Ibrahim, Yousef , Dzitac, Pavel , Mazid, Abdul Md
  • Date: 2015
  • Type: Text , Conference proceedings
  • Full Text: false
  • Description: Motor vehicle accidents are one of the main killers on the road. Modern vehicles have several safety features to improve the stability and controllability. The tire condition is critical to the proper function of the designed safety features. Under or over inflated tires adversely affects the stability of vehicles. It is generally the vehicle's user responsibility to ensure the tire inflation pressure is set and maintained to the required value using a tire inflator. In the tire inflator operation, the vehicle's user sets the desired value and the machine has to complete the task. During the inflation process, the pressure sensor does not read instantaneous static pressure to ensure the target value is reached. Hence, the inflator is designed to stop repetitively for pressure reading and avoid over inflation. This makes the inflation process slow, especially for large tires. This paper presents a novel approach using artificial neural network based technique to identify the tire size. Once the tire size is correctly identified, an optimized inflation cycle can be computed to improve performance, speed and accuracy of the inflation process. The developed neural network model was successfully simulated and tested for predicting tire size from the given sets of input parameters. The test results are analyzed and discussed in this paper. © 2015 IEEE.

Development of embedded FBG sensors for SHM systems

Smart aerospace composite structures

Industry-led mechatronics degree development in regional Australia

  • Authors: Ibrahim, Yousef , Kahandawa, Gayan , Choudhury, Tanveer , Mazid, Abdul Md
  • Date: 2017
  • Type: Text , Conference proceedings , Conference paper
  • Relation: 2017 IEEE International Conference on Mechatronics, ICM 2017; Gippsland, Australia; 13th-15th February 2017 p. 419-424
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  • Description: This paper presents a technique that was used in the recent development of a new Mechatronics degree in Australia. This technique addressed the local industry needs and the available resources for a well-balanced Mechatronics degree program. The degree development was based on project-based learning and industry engagement. The development of the new Mechatronics degree was made possible via a State Government grant of AU$2.4 Million which was matched by industry contribution of AU$10 Million in cash and in-kind. Since industry was a major stake holder in this degree, a specific industry survey was conducted to check the desired graduates attributes, from industry point of view. The results of this survey is also included in this papers. In addition, the program also addressed the regional industry's challenge of retaining qualified engineers via a clear pathway program for students knowledge and skills development. This paper presents industry's anticipated outputs of the academic Mechatronics program. In addition the paper also discusses the mechanisms adopted for the development of this new degree. The developed fully integrated Mechatronics program was founded on the realisation that if a person undertook a mechanical degree followed by an electronics degree followed by a computer science degree, that person is, still, NOT a Mechatronics engineer. © 2017 IEEE.
  • Description: Proceedings - 2017 IEEE International Conference on Mechatronics, ICM 2017

Novel tire inflating system using extreme learning machine algorithm for efficient tire identification

  • Authors: Choudhury, Tanveer , Kahandawa, Gayan , Ibrahim, Yousef , Dzitac, Pavel , Mazid, Abdul Md , Man, Zhihong
  • Date: 2017
  • Type: Text , Conference proceedings , Conference paper
  • Relation: 2017 IEEE International Conference on Mechatronics, ICM 2017; Gippsland, Victoria; 13th-15th February 2017 p. 404-409
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  • Description: Tire inflators are widely used all around the word and the efficient and accurate operation is essential. The main difficulty in improving the inflation cycle of a tire inflator is the identification of the tire connected for inflation. A robust single hidden layer feed forward neural network (SLFN) is, thus, used in this study to model and predict the correct tire size. The tire size is directly related to the tire inflation cycle. Once the tire size is identified, the inflation process can be optimized to improve performance, speed and accuracy of the inflation system. Properly inflated tire and tire condition is critical to vehicle safety, stability and controllability. The training times of traditional back propagation algorithms, mostly used to model such tire identification processes, are far slower than desired for implementation of an on-line control system. Use of slow gradient based learning methods and iterative tuning of all network parameters during the learning process are the two major causes for such slower learning speed. An extreme learning machine (ELM) algorithm, which randomly selects the input weights and biases and analytically determines the output weights, is used in this work to train the SLFNs. It is found that networks trained with ELM have relatively good generalization performance, much shorter training times and stable performance with regard to the changes in number of hidden layer neurons. The result represents robustness of the trained networks and enhance reliability of the mode. Together with short training time, the algorithm has valuable application in tire identification process. © 2017 IEEE.
  • Description: Proceedings - 2017 IEEE International Conference on Mechatronics, ICM 2017

Detecting splicing and copy-move attacks in color images

  • Authors: Islam, Mohammad , Karmakar, Gour , Kamruzzaman, Joarder , Murshed, Manzur , Kahandawa, Gayan , Parvin, Nahida
  • Date: 2018
  • Type: Text , Conference proceedings
  • Relation: 2018 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2018; Canberra, Australia; 10th-13th December 2018 p. 1-7
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  • Description: Image sensors are generating limitless digital images every day. Image forgery like splicing and copy-move are very common type of attacks that are easy to execute using sophisticated photo editing tools. As a result, digital forensics has attracted much attention to identify such tampering on digital images. In this paper, a passive (blind) image tampering identification method based on Discrete Cosine Transformation (DCT) and Local Binary Pattern (LBP) has been proposed. First, the chroma components of an image is divided into fixed sized non-overlapping blocks and 2D block DCT is applied to identify the changes due to forgery in local frequency distribution of the image. Then a texture descriptor, LBP is applied on the magnitude component of the 2D-DCT array to enhance the artifacts introduced by the tampering operation. The resulting LBP image is again divided into non-overlapping blocks. Finally, summations of corresponding inter-cell values of all the LBP blocks are computed and arranged as a feature vector. These features are fed into a Support Vector Machine (SVM) with Radial Basis Function (RBF) as kernel to distinguish forged images from authentic ones. The proposed method has been experimented extensively on three publicly available well-known image splicing and copy-move detection benchmark datasets of color images. Results demonstrate the superiority of the proposed method over recently proposed state-of-the-art approaches in terms of well accepted performance metrics such as accuracy, area under ROC curve and others.
  • Description: 2018 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2018

Passive detection of splicing and copy-move attacks in image forgery

  • Authors: Islam, Mohammad , Kamruzzaman, Joarder , Karmakar, Gour , Murshed, Manzur , Kahandawa, Gayan
  • Date: 2018
  • Type: Text , Conference proceedings , Conference paper
  • Relation: 25th International Conference on Neural Information Processing, ICONIP 2018; Siem Reap, Cambodia; 13th-16th December 2018; published in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 11304 LNCS, p. 555-567
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  • Description: Internet of Things (IoT) image sensors for surveillance and monitoring, digital cameras, smart phones and social media generate huge volume of digital images every day. Image splicing and copy-move attacks are the most common types of image forgery that can be done very easily using modern photo editing software. Recently, digital forensics has drawn much attention to detect such tampering on images. In this paper, we introduce a novel feature extraction technique, namely Sum of Relevant Inter-Cell Values (SRIV) using which we propose a passive (blind) image forgery detection method based on Discrete Cosine Transformation (DCT) and Local Binary Pattern (LBP). First, the input image is divided into non-overlapping blocks and 2D block DCT is applied to capture the changes of a tampered image in the frequency domain. Then LBP operator is applied to enhance the local changes among the neighbouring DCT coefficients, magnifying the changes in high frequency components resulting from splicing and copy-move attacks. The resulting LBP image is again divided into non-overlapping blocks. Finally, SRIV is applied on the LBP image blocks to extract features which are then fed into a Support Vector Machine (SVM) classifier to identify forged images from authentic ones. Extensive experiment on four well-known benchmark datasets of tampered images reveal the superiority of our method over recent state-of-the-art methods.