Image Forgery Detection and Localization using DCT-based forensic analysis approach
*Monika 1, Dipali Bansal 2
1 Manav Rachna International Institute of Research and Studies, Faridabad, India.
2 Manav Rachna International Institute of Research and Studies, Faridabad, India.
1[email protected], 2[email protected] ,
Abstract
Digital image tempering is widespread because of software and devices that manipulate image information without any traces are easily available for high- performance image editing. Now everything is online for a day and digital images are presented as evidence of any event, documentation where forgery hides its traces.
Existing techniques for forgery detection are based on the higher complexity of computational costs. The technique proposed is robust even with pre- and post-processing operations for automatic detection and localization of specific artifacts. We proposed a DCT supported technique to obtain features from each block of images that reduce the block dimension and assist in lexicographic sorting. Tampered blocks of images are compared with predefined threshold values based on robust parameters to detect similar blocks in reduced time. Experimental results are robust to multiple forgeries with low computational complexity and retained significance of image information. On several images that are affected by different forgery types, we show our method robustly.
Keywords: Block feature extraction, Digital Authentication, Digital Image forensics, DCT, Forged region localization.
1. Introduction
O
ne of the most important means of exchange of information is the digital image in the current scenario. Because of the current development of media editing technology, unexperienced people can easily manipulate the meaning of image information. Thus, the authentication of digital images plays an important role in the daily routine of the whether educated or uneducated life of each individual as WhatsApp data sharing between many users from anywhere in just a few seconds via the internet facility and any unauthorized user can easily change the meaning of an image. Digital forgeries with various security and authentication threats that were easily doctored through digital programming without leaving any clues that the human eye doesn't easily identify. We need an expert forensic science system to authenticate images to detect such artifacts. Many researchers have proposed numerous methodologies of detection [1-5], having, image processing of colour changes, feature extraction from various segments such as overlapping or non- overlapping area, high entropy area, the matching process of features, post-processing for inconsistency results. Therefore, forgery detection becomes a major concern in most areas such as medical records, multimedia, journalism, evidence in criminal investigation courtrooms, educational academic departments, e-services, banking transactions, real- estate documents, financial documents, video recordings, etc. Active and passive approaches are supported as existing forgery detection techniques are classified as Digital Signature and the digital marking of water. Digital image signature verification creates a unique signature with some important information about the original images. During authentication, this unique signature was easily identified and also required especiallyexpensive software for the testing process. Another is digital watermarking that requires digital devices to insert watermarking before and after capturing images. Therefore, blind forgery detection by assuming the calculations of statistical measurements overcomes the limitations of the active approach in better ways. The passive approach uses forged region detection statistics and is better than active approaches but still needs a reliable system of forgery detection to improve the belief that applications in the real world are truthful.
Digital forensic image techniques work incoherently without any traces such as watermark, signature, etc. Different forgery categories made some important information easy to conceal or duplicate due to low cost and easily accessible software and hardware editing devices. This reduces the impact of the digital age. Several methods for detecting forgeries have been proposed using various schemes but still have significant limitations as the forged regions have nearly the same geometric properties like texture, colours, and objects that are not easily identified by human eyes. Digital forensics must be robust to geometric translation and illumination changes like noise, rotation, compression scaling, and blurring. The main objective is to minimize the complexity by creating feature vector smaller and thus the robust extraction of the feature against the digital image post-process.
CMFD mainly classified supporting feature extraction methodologies used by numerous researchers as mainly block-based, segmentation-based, and key-point-based schemes.
Block-based strategies extract features from overlapping image blocks and start matching feature vector processes that solve the forged region problem by transforming calculations. For the extraction of features, many authors use frequency domain techniques such as LPT, FMT, DWT, DCT, etc. Many authors are also using moments like Hu, Zernike, and Blur. Due to the time consumption examination of all overlapped blocks, the main drawback is high computational complexity and expensive due to the high-dimensional vector feature analysis.
SIFT & SURF tempered image descriptors are extracted from key-point strategies. For key-point extraction, many authors use distinct cluster techniques. This is better than block-based because it means only key-points for vector Harris corner point, binary robust invariant scalable key-points, and slope histogram based on multi-bolster district request.
Computational complexity is less but the absence of forged region missed entirely due to sparsely covered matched key-points or loss JPEG format used and is not effective in smooth regions. Segmentation-based methods: Super-pixel image group used for the matching process of the SIFT key-point. An irregular block represents the detection of bettor forgery and reduced computational complexity. As initial, super-pixel determination is difficult, the output is not ideal. They all help to minimize computational complexity in real-time processing and maximize robustness to detect forgery.
We propose a framework for this paper that recognized falsification based on comparable extraction of elements, First, we obtained the overlapped image blocks and extracted similar features from the blocks to show conformed forgery using the best-optimized threshold value by performing numerous experiments using DCT-PHASE instead of Euclidean distance or cross-correlation. This is also robust for multiple forgery detection without degrading the image quality with 99.03% accuracy in less complexity and less time consumption. Illustration 1. Shows the case of manipulated images. Fig1 (a) is the suspected images in which the forged images with the threshold are shown as Fig1 (b) and Fig1(c) respectively shows the output images.
The complete paper arranged as the first section examined the existing forensic image methodologies to date. The proposed algorithm in the second section explained any kind of image forgery. Under different conditions, the third section analysed experimental results. Finally, conclusions have been drawn and future scopes have been presented.
2. Related Research
Different researchers have implemented different methodologies for the last 20 years.
Fridrich et al. [1] [2003] proposed an initiative to detect CMFD using a block-based approach through DCT and numerous analysts used the paper of Friedrich as a standard forgery detection discovery in further writing but ready to recognize forgery if rotation occurs up to 5 degrees. Every year, many papers were published to present CMFD as the most active approach in the forensic image. Existing methodologies from 2003 to the present use similar frameworks such as dividing images of fixed size overlapped blocks, the extraction mechanism applies to extract similarities between the respective blocks.
Popescu et al. [2] [2004], proposed a blind approach using the main component analysis for forgery detection to obtain a vector feature that was robust against compression attacks using different feature representation. Luo et al. [3] [2006] used the same procedure by dividing blocks into four sub-sets that are colour-based sub-block division approaches in simple forgery detection as showing compression robustness only by using 1 * 7 vector size and limited results in post-processed images. Mahdian et al. [4] [2007], the Authors used a blur invariant approach after processing to detect forgery. Authors used blur moment only for colour blocks with 1 * 72 used vector size and used PCA for reduced vector function via K-tree to diagnose forged regions of various sizes and shapes for dedicated interpolated images to perform resampled image operations. Kang et al. [5]
[2008], Single value decomposition inspected over blocked images to obtain decreased measurements to achieve vigorous results compared to noise distortions. Bayram et al.
[6] [2009], Authors used Fourier Mellin Transform with log 1D projection to obtain forged area location if revolutionary assaults invariant to small regions were to occur. The authors used bloom filters in the matched detection process but have limitations on resized images. Lin et al. [8] [2009], proposed the block-based extraction algorithm with pre-sorting average intensities. Ryu et al. [8] [2010], the proposed moment for block- based extraction of the feature matching the rotation obtained as well as pre-rotated images invariant to rotational transformation. Amerini et al. [9] [2011], Implemented an invariant scale feature transform method for forgery detection and geometric transformation location even in clustering key-point displays high TPR and low FPR even in compressed and noisy images. Zhao et al. [10] [2013], Presented discrete cosine transformation and singularly valued decomposition for forgery detection and used image block of 2 * 2 non-overlapped sizes and processed by SVD to obtain maximum value.
Authors compare explicitly with existing methodologies to show robust accuracy and low false negatives. Chang et al. [11] [2013], Used a block-based approach to detect falsification but fails in the case of post-processing geometry. Cozzolino et al. [12]
[2015], a Proposed algorithm using exemplar-based object removal awakening shows reduced runtime without affecting accuracy. Pan et al. [13] [2010], Used SIFT for rotated duplication detection feature extraction using SIFT descriptors to analyse geometric transformations, including RANSAC used for forgery detection over-compressed image post-processing. Gilinsky et al. [14] [2013], It also reduces the proposed SIFT-pack- based algorithm to obtain sift descriptors to reduce runtime processing and storage area.
Shivakumar et al. [15] [2011], Implemented a Robust speed-up feature with less false- positive rates for duplicated region detection, especially in high-resolution images. Chen et al. [16] [2013], Used key-point extraction using the point of interest of Harries Corner and achieving a very small circular forged region. Silva et al. [17] [2015], Implemented multi-scale digital image analysis through voting processes and demonstrates robustness in geometric image processing. Some very small texture structure forgeries give false detection. Authors address the false-positive rates to obtain an accurate analysis of human interpretations. Daugman et al. [18] [1980], Presented block-based approach to obtain a Gabor-oriented histogram to achieve frequency resolution and feature extraction space resolution helps to detect forgery. Huang et al. [19] [2011], Implemented a key-point
extraction of overlapping block images to improve runtime efficiency by quantizing vector detection. The authors applied the closest neighbour approach to matching a similar vector process, but for a small forged region, the algorithm was limited. Liu et al.
[20] [2011], used the hu invariant moment for forgery detection. The authors used circular overlapping blocks to obtain hu moment and apply gauss decomposition from each block using only the first four moments. Myrna et al. [21] [2007], Proposed polar log algorithm transformation using DWT to obtain a reduced feature size measurement for overlapped blocks using a matched procedure phase correlation. Wu et al. [22] [2010], Authors presented log-polar transforming Fourier through circular overlapping blocks to extract feature with standardized cross-spectrum to obtain abnormal similarity in each blocked image and examined by the fuzzy mechanism. Wang et al. [23] [2012], Authors used DWT for block size 1 * 30 extraction to show better results in noisy images. Zhang et al.
[24] [2010], Presented voting strategy for forged images based on pixel matching sub- window search methodology to locate forged areas under geometric distortions. Xu et al.
[25] [2010], Authors used to speed up robust descriptors features and randomly divide key points and approach closest neighbours for matching processes for all subgroups that have compressed and noisy images for testing process geometric operations. Under the uniform regional structure, the algorithm displays highly complex computations. Lee et al. [26] [2015], Presented segmented image detection algorithm received from different sources and displays similarity parameters. Lin et al. [27] [2011], Suggested the implementation of camera response image composition detection and reviewed all DCT coefficients to detect JPEG forged images. Li et al. [28] [2015], Displays forgery detection grid block artifacts and results show low false-positive rates. The authors used the DWT & SVT approach to their implementation by lexicographically sorting. Qu et al.
[29] [2008], Implemented an independent component analysis algorithm to display splicing even for compressed images with double JPEG. Bashar et al. [30] [2010], achieve robust detection, authors used DWT & KPCA to extract features. Lin et al. [31]
[2005], the Authors implemented a radix-based algorithm for the extraction of features but shows limited post-processing results. Li et al. [32] [2008], Authors enhance the rotation invariance used angular projection, but with limitations in small regions and high false-positive rates result. Christlein et al. [33] [2010], Used affine transformation selection for block-based extraction feature which is invariant to scaling, post-preparation rotation type. Ustubioglu, B. et al. [34] [2016], proposed methods of forgery detection using the generalized Benford Law and Discrete Cosine Transformation to achieve limited feature vectors helps to automatically obtain optimized threshold values that are robust for all post-processing operations. Higher accuracy ratio and lower FN value were proposed by authors only up to p=0.99 and f=0.04. Xiuli bi et al. [35] [2018], Presented feature correspondence using upgraded coherence delicate hazing and local bidirectional coherence error to obtain sensitive results through the different iterative procedure to select the specified threshold to achieve rapid identification for real-time viability with final measurement up to 96.63 percent. Bansal, D. et al. [98-106], presented different mechanism to achieve optimized results for real time applications.
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3. Proposed Algoritm
1. Input images are converted to M*N size gray images. Use I=0.299R + 0.5879 + 0.114B.
2. The fixed size of the B*B window slides one component from the top left corner to the base right corner and partition I to (M-N+1) (N-B+1) square Blocks.
3. For each square, block applies a discrete cosine transform, the quantized coefficient frameworks with a zig-zag output to a row vector and is then truncated to only (p*b) components.
4. All vectors arranged for frame (M-N+1) (N-B+1) PB elements in the ascending request.
5. A fixed B*B window slides one pixel with the upper left corner to the base right corner and the square BLOCKS partition I (M-N+1) (N-B+1).
6. Each row tests its adjacent row where similarity is the distance between two corresponding blocks.
7. The corresponding process is performed by the shift vector and the threshold is the area marked on the map.
Proposed Transformation Techniques Details Using Enhanced DCT:
Transform coding in image processing depends on the fact that image pixels display correlations with their adjacent pixels. To map this correlated data into uncorrelated coefficients, we need such a transformation. DCT having a strong energy compaction property and de-correlation property as de-correlation is removing uncorrelated coefficients of transformation that can be encoded independently.
DCT represents an image in the sinusoidal as a sum of variable magnitudes and frequencies. As a result, DCT has been adapted to de-correlate image data. DCT's main advantage is that the signal energy was concentrated only in the first few coefficients compared to other coefficients and noise, changes in high-frequency lighting do not significantly affect the first coefficients.
DCT used to obtain a frequency component from a signal that helps in the processing of images such as JPEG compression. It gives the corresponding image pixel equivalent frequency value. These pixels are divided into N*N block sizes where N depends on image type and equation 1-9 shows all mathematical calculations for image processing.
This selection of block size helps to reduce the complexity of time as DCT has been applied to each colour component of each pixel.
1D-DCT Defined as:
C(u)=α(u) ……….………1
For u=0, 1,2,3…...N-1………..
Inverse DCT defined as………
f(x)= ………..……..2
For x=0, 1, 2, 3…...N-1………..
In equation [1] and [2] α (u) is defined as……
α (u) ={ for u=0 ………..3 = { for u≠0
DC Coefficient defined from equation 1 is-
U=0, ………..4
2D-DCT is a direct extension of 1D-DCT and defined as-
2D-DCT of an M*N image is given by equation-
C (u, v) = ………5
Where α (i) = { for i=0 …………..………..……..6 = { for i≠0
For u=0, 1, 2, 3…...N-1………..
For v=0, 1, 2, 3…...M-1………
Value of c defined DCT Coefficients of the images
………7
Following DCT equations with sign operator, no pixel information is lost because it is equivalent to the cosine operator defined as -
(u, v) = ………8
= …….………9
Evaluation Matrices
Based on the True Positive, True Negative, False Positive and False Negative values
• False Positive Rate (FPR) =
• False Negative Rate (FNR) =
• Sensitivity (TPR) =
• Specificity (TNR) =
• Precision =
• Accuracy =
• Final -score =
4.
Analysis and Results of Experiments
Experiments were conducted on various datasets that were freely used online to ensure inconsistency in the output of our method. Datasets are taken from www.grip.unina.Its images with a resolution of 3000 * 2300 pixels were 48 uncompressed PNG. Second, MICC M220 images with 768 * 1024 pixel resolution. We have proposed algorithms and compared existing methodologies such as DCT based Fridrich et al. [2003], SIFT based I.
Amerini et al. [2013], PCA-based Popescu and Farid et al. [2004], FMT-based [2009], Ryu et al. ZERNIKA [2010], LIN-based [2009].
Different post-processing effects have processed manipulated images such as first, the different quality factor of 50, 65, 75, 85, and 90. Secondly, rotation with different angles 2; 6; thirdly, with a different scaling factor of 0.8.0.95, 1025, 1.50. Finally, different brightness changes with different resolution of [0.01, 0.089], [0.02,09], [001,0.07]. The result of the output will only take 6sec to show location forgery.
5. Conclusion
Copy moving forgery detection algorithm implemented with all image restrictions and works without any suspected image details such as a digital watermark or digital signature.
Proposed work compared to existing methodology to demonstrate that our approach takes fewer features for each block by reducing feature dimensions matching processes with more robustness, reliability, integrity, and efficiency in the experimental results against all post-processing operations that help to hide forgery and reduce computational complexity costs.
False match removal criteria apply to all image types. Hopefully, this work can be used for multimedia forensic sciences in many applications. We are currently working on these areas to achieve better results in case of a highly geometric disturbance of image quality.
Table1. Details of simulation
Method References Time (s) Accuracy (%)
PROPOSED METHOD 6 99.03
DWT Bashar et al. [30] [2010] 96.60 89.22
BLUR Mahdian and Saic et al. [4]
[2006]
113.19 84.09
HIERARCH-SIFT Amerini et al. [9] [2011] 142.13 73.95
PCA Popescu and Farid et al. [2]
[2004]
180.11 83.47
DCT Fridrich et al. [1] [ 2003] 296.74 86.00
Malty scale analysis Silva, E et al. [17] [2015] 515 90
Over-segment C.-M. Pun et al. [39] [2010] 683 88
KPCA Bashar et al. [30] [2010] 880.00 87.44
SURF B.L. Shivakumar, et al. [15]
[2011]
1052 87
SIFT I. Amerini et al. [10] [2013] 1098 80
ZERNIKE2 Ryu et al. [87] [2013] 1418.68 90.10
ZERNIKA Ryu et al. [8] [ 2010] 7065 88.08
SIFT, Descriptor Christlein et al. [42] [2012] 178.62 83.52
Zernike Moment Ryu et al. [87] [2013] 969.07 96.00
1-D Descriptor derived from log polar map
Bravo-Solorio and Nandi [86]
[2011]
1653..65 91.20
SIFT Descriptor and
Moment of Zernike
Zheng et al. [57] [2016] 780.66 83.12
SIFT, Descriptor Li et al. [28] [2015] 1215.37 82.19
SIFT, Descriptor Pun et al. [39] [2015] 532.45 97.96
Moment of polar comple x transformation, Zernik e moment, Fourier- Mellin transformation
Cozzolino et al. [12] [2015] 191.31 95.63
Coherency error. Xiuli Bi et al. [35] [2018] 74.17 96.63
Table 2 Details of Analysis.
Existing methodology
Quantity of blocks Dimensions of f eatures
Extraction of features
Matching of features
PCA [2004] 255,025 64 31.792 44.841
DCT [2003] 255,025 32 476.874 40.281
FMT [2009] 247,009 45 58.266 41.502
Zernike [2010] 247,009 12 40.821 38.388
Lin [2009] 247,009 9 3.017 37.303
SIFT [2011] 2700 key-points 128 4.357 1.235 Jen-Chun
Lee [2015]
247,009 12 36.771 38.472
PROPOSED 250,012 11 37.10 705
Table3. Analysis of the CMFD brightness
change
[0.01, 0.95] [0.01, 0.9] [0.01, 0.8]
TPR-Jen-Chun Lee [2015]
0.986 0.975 0.953
FPR-Jen-Chun Lee [2015] FPR
0.024 0.036 0.043
TPR-
PROPOSED
0.99 0.98 0.98
FPR-
PROPOSED
0.011 0.024 0.025
Table4. Comparison analysis of existing complexity techniques.
Methods Feature Length of
Feature
Li et al. [28] [2015] LBP 256
W. et al. [94] [2010] DAISY 200
Amerini et al. [10] [2013] SIFT 128
Fridrich et al. [1] [2003] DCT 64
Bayram et al. [6] [2009] FMT 45
Popescu and Farid [2] [2004] PCA 32
Raj, R. and Joseph, N [85] [2016] FMT 25
Ze S.-J. Ryu [87] [2013] ZM-c & ZM-p 12
Mahmood et al. [79] [2016] DCT & KPCA 10
Toqeer Mahmood et al. [74] [2017] SWT & LBPV 10 S. Teerakanok and T. Uehara [83] [2019] PCT-c, PCT-p &
PCET-p
10
PROPOSED TECHNIQUE DCT 11
Figure 1. Classification of techniques for feature extraction
Figure2. Proposed algorithm workflow.
Figure 3 Images received as input.
Figure4. Forgeries with mapped regions detected
Figure5. Outputs Detection of Forgeries.
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Authors Profile
Ms Monika received her B. Tech degree in application of Electronics and Communication Engineering in 2007and M. Tech degree in ECE in 2011 both from MDU Rohtak, Haryana. From 2007 to 2019, she was an Assistant Professor in Manav Rachna International Institute of Research and Studies, Faridabad, NCR, India. She is currently working towards the PhD degree in ECE at MRIIRS FARIDABAD. Her research interest includes image processing, image forensics, Digital Logics.
Dr Dipali Bansal is the Director-IQAC and Associate Dean - Academics with Manav Rachna International Institute of Research and Studies, Faridabad, NCR, India. Dr Bansal is a doctorate in Bio signal processing from Jamia Milia University, New Delhi and an upcoming and young scientist. She has got a distinguished career in teaching and industry spanning 22 years and her research work has found prominent recognition and has been published in many national and international journals and conferences (80 papers). She has attended many International conferences abroad primarily at Washington D.C and Los Angeles USA and Italy (Florence). She is a Reviewer of many journals including the journal of Medical and Biological Engineering and Computing (Springer), Computers in Biology and Medicine, Elsevier Journal (Science Direct), and Journal of Circuits, Systems, and Signal Processing.
