Bibliography of digital image anti‐forensics and anti‐anti‐forensics techniques

IET Image ProcessingVolume 13, Issue 11 p. 1811-1823 Review ArticleFree Access Bibliography of digital image anti-forensics and anti-anti-forensics techniques Muhammad Ali Qureshi, The Islamia University of Bahawalpur (IUB), 63100 PakistanSearch for more papers by this authorEl-Sayed M El-Alfy, Corresponding Author alfy@kfupm.edu.sa King Fahd University of Petroleum and Minerals (KFUPM), Dhahran, 31261 Saudi ArabiaSearch for more papers by this author Muhammad Ali Qureshi, The Islamia University of Bahawalpur (IUB), 63100 PakistanSearch for more papers by this authorEl-Sayed M El-Alfy, Corresponding Author alfy@kfupm.edu.sa King Fahd University of Petroleum and Minerals (KFUPM), Dhahran, 31261 Saudi ArabiaSearch for more papers by this author First published: 01 August 2019 https://doi.org/10.1049/iet-ipr.2018.6587Citations: 2AboutSectionsPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onEmailFacebookTwitterLinked InRedditWechat Abstract With the massive increase of online content, widespread of social media, the popularity of smartphones, and rise of security breaches, image forensics has attracted a lot of attention in the past two decades alongside the advancements in digital imaging and processing software. The goal is to be able to verify authenticity, ownership, and copyright of an image and detect changes to the original image. However, more sophisticated image manipulation software tools can use subtle anti-forensics techniques (AFTs) to complicate and hinder detection. This leads security professionals and digital investigators to develop more robust forensics tools and counter solutions to defeat adversarial anti-forensics and win the race. This survey study presents a comprehensive systematic overview of various anti-forensics and anti-AFTs that are proposed in the literature for digital image forensics. These techniques are thoroughly analysed based on various important characteristics and grouped into broad categories. This study also presents a bibliographic analysis of the-state-of-the-art publications in various venues. It assists junior researchers in multimedia security and related fields to understand the significance of existing techniques, research trends, and future directions. 1 Introduction Over the past years, several advancements have been witnessed in multimedia, communication, mobile, and storage systems technologies. This has led to a massive increase in online sharing of digital images over social and professional networks such as Instagram, Flickr, Pinterest, and Google Photos. For instance, Table 1 demonstrates the number of unique monthly visitors of top 15 popular online image-sharing websites as of May 2018 [http://www.ebizmba.com/articles/photo-sharing-sites]. Moreover, there are many other generic file sharing systems such as Google Drive, OneDrive, Dropbox etc. Images were often considered as a reliable visual source for recording and authenticating important events and activities from personal to official levels. However, with the availability of modern image editing software tools, such as Adobe Photoshop, Corel Draw, and Pixlr, images become prone to more manipulation than ever before. Various ethical or unethical reasons can drive the changes. This makes the trustworthiness of digital images a major concern. At certain circumstances, these changes are destructive and it is crucial to detect their presence and locate the altered regions in the image [1]. Table 1. Examples of most popular online image-sharing websites demonstrating the estimated number of unique monthly visitors Image-sharing sites Monthy unique visitors (in million) instagram 100 imgur 87.5 flickr 80 photobucket 60 deviantArt 45 shutterfly 20 tinyPic 9 weHeartIt 8.1 imageShack 8 imageVenue 5.75 imageBam 5.5 smugMug 5 picasa 4.5 twitpic 4 snapfish 3.4 The history of image forensics also covers a lot of interesting examples where the digital photos of politicians, national heroes, or celebrities were manipulated for ethical/unethical reasons. Fig. 1 shows an example of a forged portrait of the former US President Abraham Lincoln in 1860 that could be considered as the first Photoshopped image. In this photo, Lincoln's head was stitched with the body of a Southern politician, John Calhoun. The intention was to portray the US President like an American hero and at that time no heroic style photo of Lincoln was captured. In this example, the original photo is shown on the right side whereas the tampered one is on the left side. Another example of a Photoshopped image is shown in Fig. 2, where Queen Elizabeth Bowes-Lyon (mother of Queen Elizabeth II) and the Canadian Prime Minister, William Lyon Mackenzie, in Banff, Alberta are shown together without King George VI in the original image. The tampering was made during the election campaign of the King to present himself in a more powerful position as standing alone with the Queen. There are several other examples of forged images over the past years. Nowadays, the quote that ‘seeing is believing’ is undoubtedly sceptical [2]. For more examples of image tampering and an overview of the historical development of image forensic, there are a number of surveys and overview papers in the literature [1, 3-7]. Fig. 1Open in figure viewerPowerPoint 1860: US President Abraham Lincoln Photoshopped portrait (right image), and source image of Southern politician John Calhoun whose body was stitched with Lincoln's head (left image) Fig. 2Open in figure viewerPowerPoint 1939: An example of doctored photo of Queen Elizabeth (right image) and original version with King George VI (left image) A variety of algorithms have been proposed for image forensic investigation of digital images. They aim at detecting image manipulation/doctoring such as image enhancement (contrast adjustment, histogram equalisation (HE), colour modification), content alteration (e.g. copy-move, copy-paste, splicing, seam carving), and geometric changes (e.g. rotation, scaling, cropping) [4, 8-14]. These algorithms are classified into various categories such as active or passive, blind or semi-blind, forgery dependent or forgery independent, and pixel-based, format-based, camera-based, physics-based or geometric-based [9, 15-18]. Active methods involve adding extra information to images as a preventive measure, which can be checked at later stages to confirm the authenticity of digital images. The examples of active methods are fragile and semi-fragile watermarks and digital signatures. These methods are intrusive and may drastically affect the content quality. They are even not suitable for images that have been already uploaded on the Internet without adding this preventive information. Passive methods are therefore used to overcome the limitations of active methods. Fig. 3 shows a tree diagram listing the different classes of passive digital image security aspects discussed in the literature. The tampering detection techniques are divided into four broad categories: (i) image copy-move forgery detection, where the source and destination for tampering image regions belong to the same image (see Fig. 4), (ii) In image splicing, the tampered regions belong to another image (see Fig. 5) [19], (iii) Image retouching is also a subtype of tampering where the details of the image are enhanced to make it more prominent, especially used by professionals to improve the quality of photographs or as a form of arts, and (iv) Finally, image inpainting which is also considered as copy-move forgery except for the difference that the source regions belong to different locations of the image [20, 21]. It is usually used to restore the missing pixels, cracks in an image or to remove some objects. Fig. 3Open in figure viewerPowerPoint Framework for grouping the different digital image security techniques Fig. 4Open in figure viewerPowerPoint Example of copy-move forgery (a) Original image, (b) Tampered image Fig. 5Open in figure viewerPowerPoint Example of image splicing (a) and (b) original images, (c) Tampered image created by combining (a) and (b) [19] Image tampering processes somehow leave some traces of manipulation in the tampered image, though these traces may be invisible to naked human eyes when the original image is not available. Image forgers may apply various techniques and tricks to hide the tampering traces making detection a bit harder or impossible by traditional forensic techniques [22-26]. Therefore, it is becoming extremely critical to consider these anti-forensics techniques (AFTs) in developing more robust image forgery detection methods. Although a lot of research has been carried in developing more robust image tampering detection techniques, little effort has been made into overcoming intelligent counterfeiters who deliberately hide the tampering traces during the forgery process to fool the detector. The AFTs, however, aim to deceive image forensic by hiding tampering fingerprints. On the other hand, to rebuild the credibility of forensics, counter counter-forensics or anti-anti-forensics is also being used. In the literature, various anti-forensics and anti-anti-forensic algorithms have been proposed to hide image manipulations, fingerprints, and countering the hidden process, respectively. After deep consultation of literature, we have found that there is no survey on image forensics so far that provides a detailed discussion on anti-forensics and anti-anti-forensics methods. The scope and main contributions of this paper are: (i) survey the state-of-the-art of AFTs and Anti AFTs (AAFTs), (ii) provide readers with a comprehensive review and analysis of 81 most relevant publications, and (iii) present a summary of main differences of available methods. The analysis of the covered publications, collected from Scopus.com and Google Scholar, has revealed that 77 of them address image anti-forensic techniques and 27 of them are concerned with image AAFTs. Fig. 6 shows the publication trend and distribution into conference proceedings and journal articles. As demonstrated in this figure the majority of publications focus on image AFTs, yet in recent years the trend of publications on AFTs is declining whereas more publications appear on AAFTs. Fig. 6Open in figure viewerPowerPoint Publication trend and distribution into conference proceedings and journal articles The rest of the paper is organised as follows. In Section 2, we discuss anti-forensic methods, while AAFTs are discussed in Section 3. Finally, we conclude the paper with some critical thoughts and future research directions in Section 4. 2 Anti-forensics techniques (AFTs) Based on the employed methodology, image anti-forensic techniques are grouped into different categories and discussed in detail in the following subsections. 2.1 Contrast enhancement (CE) In cut-and-paste (or splicing) image forgery, the pasted regions and the background of the original image usually have variations in the contrast level due to different lighting conditions between the two images. To hide this visual difference, forged images are typically post-processed using CE operators to adjust the global contrast and brightness. In order to detect retouched images by CE, the image forensic analysis depends on the assumption that the histogram of a natural image is generally smooth whereas the histogram of a contrast-enhanced image has a wide envelope with peak and gap artefacts [27]. Thus, the CE detectors utilise features derived from the first-order statistics such as peaks and gaps analysis of image histograms [27-30]. To fool histogram-based CE forensic detectors, different techniques have been proposed as explained in the following subsections. 2.1.1 Trace hiding The methods in this category hide the peak-gap artefacts in image histogram to be undetectable by blind image forgery detectors. They use variants of CE operators by performing local random dithering operations with the addition of Gaussian noise. Cao et al. [31] performed local random dithering operations in the primary mapping of CE to hide the peak-gap artefacts in the pixel grey-level histogram of the contrast-enhanced image. The same authors in [32] also proposed trace forging and trace hiding attacks against CE detectors. The results showed significant reductions in detection accuracy compared to [27, 28]. Kwok et al. [33] used Internal Bit Depth Increase method for further improvement in concealing peak and gap artefacts with good visual image quality. 2.1.2 Histogram remapping In these methods, the histogram of the contrast-enhanced image is modified according to that of the original image using a pixel mapping technique. In this regard, Barni et al. [34, 35] proposed a universal anti-forensic technique to counter histogram-based manipulation detectors. A general optimal framework was proposed by Comesana Alfaro and Pérez González [36] in which the authors derived the optimal changes to the histogram to defeat existing histogram-based forensic detectors. Since CE detectors based on first-order statistics are shown to be less robust by the above-mentioned anti-forensic techniques [37], CE detectors based on other metrics were proposed. Lin et al. [30] used the inter-channel dependency due to colour image interpolation to detect CE in an image. Another recent algorithm proposed by De Rosa et al. [37] used the Grey Level Co-occurrence Matrix (GLCM) of an image to determine whether it is CE manipulated or not. The use of second-order statistics of the image is shown to be more effective against anti-forensic techniques targeted at histogram-based detectors. The performance evaluation was carried using a large database and results were compared with the first-order [27] and second-order [37] statistics-based CE detectors. It is, therefore, suggested to use a third or even higher-order statistics to develop more robust and efficient CE-based AFTs. Ravi et al. [38] proposed an effective AFT for CE using a variation of the total variation (TV) norm optimisation. The algorithm performed CE on an image by solving an optimisation problem. The problem was formulated such that even though the solution looks contrast enhanced with high visual quality, it has lower TV and characteristics similar to the original image. 2.2 Unsharp masking Unsharp masking (USM) is a type of sharpening that is commonly used in the final stages of image retouching to disguise the blurring alterations in an image. It is available in popular image editing tools such as Photoshop and GIMP. However, it can be detected using techniques such as those proposed in [39, 40]. Laijie et al. [41] proposed an AFT for USM sharpening detection by adding random local dithering to remove the overshoot artefacts in image edges and the abrupt change in the histogram. The authors demonstrated the effectiveness of the technique with experimental work carried out on a large image database with different parameter settings and without degradation in the image visual quality. 2.3 Median filtering Median filtering (MF) is another preprocessing step involved in image forensics to remove the noise with significant changes in the image details. Kang et al. [42] proposed a robust method for detecting median filtering based on an autoregressive (AR) model. To counter median filtering detectors, Nguyen et al. [43] explored the characteristics of median filtering operation based on the local features derived from image blocks in terms of median distribution, the occurrences of block centre grey level, the quantity of grey levels, distribution of centre grey level in sorted grey levels, and the first occurrence of block centre grey level in sorted grey levels. The fingerprints of median filtering are concealed in uncompressed images by modifying random pixels based on the distribution of block median and occurrence of block centre grey level. Smooth blocks are expected to have block centre value occurring more frequently and high probability of having more than one median value within a block. So the blocks with high values for the distribution of block median and occurrence of block centre grey level are discarded and a random small perturbation is added to those blocks amongst the rest which have standard deviation higher than the threshold. Wu et al. [44] proposed an AFT for median filtering by modelling the distribution of pixel differences as a Generalized Gaussian Distribution (GGD). The GGD parameters were estimated using simple regression. Similarly, Fan et al. [45] proposed an optimisation framework considering the distribution of pixel differences as a prior term for median filtering anti-forensics and image reconstruction. Nguyen et al. [43] proposed an AFT for MF by adding noise to highly textured blocks. Fontani and Barni [46] proposed an optimisation framework for anti-forensics of median filtering using linear convolution filters with size while maintaining the image quality in terms of Peak Signal-to-Noise Ratio (PSNR) while reducing the cost of features that detect non-linear filtering. The algorithms discussed in [43-46] performed well for a specific MF detection technique. A comprehensive performance evaluation of different MF detectors [47-49] against anti-forensic attacks was provided by Sharma et al. [50]. The technique is different from [45] in the sense that the work in [45] hides the MF traces in a postprocessing step without using the original image information whereas in [50] both original and median filtered image information are used to hide the traces of MF in the resultant image. A recent approach based on the generative adversarial network (GAN) is proposed in [51] to deceive existing median filtering techniques. 2.4 Anti-forensics for chromatic aberrations The Lateral Chromatic Aberration (LCA) is an imaging trace introduced in optical imaging systems due to the inability of the camera lens to focus all wavelengths of light at the same point. It results in relative contraction or expansion between colour channels of an image and produces imperceptible colour fringes around image edges. In earlier work, the localised inconsistencies in LCA have already been used to detect image splicing in digital images [52-55]. To hide the traces of LCA presenting in a tampered image due to splicing, Mayer et al. [56] proposed an anti-forensic technique by calculating the LCA locally at an image location. The deviations from the estimated chromatic aberration due to tampering was removed from the tampered image. The results showed the effectiveness of the technique in hiding tampering traces in digital images. 2.5 Countering camera identification Source identification based image forensics are also being used. Among these, Photo-Response Non-Uniformity (PRNU) sensor noise pattern-based source detection is efficient due to the uniqueness of PRNU noise pattern for every digital camera. Sengupta et al. [57] used median filtering to suppress the PRNU noise to fool the PRNU sensor noise-based source identification. The results showed the effectiveness of the scheme in terms of source anonymity using inverse of Peak-to-Correlation Energy ratio measure compared to the state-of-the-art. Similarly, Villalba et al. [58] proposed a two-stage algorithm to counter the forensic techniques and forge the image source based on sensor noise and wavelet transform such that it points to an unrelated device without requiring physical access to the source camera or its related information. Thus, this algorithm provided a more realistic approach to destruct the identifiable data and falsify the image camera identification. Deep learning has recently attracted much attention, especially in computer vision applications. It has been found effective to design a camera identification model based on employing a Convolutional Neural Network (CNN) [59]. However, Chen et al. [60] have recently proposed a new AFT based on a GAN to fool the CNN-based camera identification model at the cost of visually imperceptible distortion introduced in the tampered images. The results showed 98% accuracy in fooling CNN-based camera model identification classifier. 2.6 Anti-forensics of colour filter array (CFA) pattern Most of today's digital cameras contain only one type of colour sensor at each pixel. Colour interpolation algorithms are then used to produce high-quality images. Identification of these colour interpolation algorithms is also used in digital image forensics. The CFA interpolation introduces periodic local correlation pattern between neighbouring pixels and can be used to find the traces of demosaicing (or colour restoration) [61, 62]. To hide the traces of these periodic local correlation, Böhme and Kirchner [63] proposed an AFT to alter the raw image pixels by introducing CFA patterns in the tampered images. The method was based on synthesising a linear dependency among image pixels while minimising the overall distortion by searching a pre-filter to estimate raw samples that are captured by the camera sensor. Finally, the entire image satisfying the linear dependency was reconstructed by estimating the raw samples using a bilinear interpolation kernel. The technique was validated on a large image database and compared with state-of-the-art techniques. The results showed the effectiveness of the CFA synthesis in hiding tampering traces without loss of image visual quality. Guangling et al. [64] proposed an image AFT based on CFA structure detection using dictionary re-demosaicing. A sparse representation-based dictionary was constructed from the tampered image. An arbitrary CFA demosaicing was performed to re-demosaic each dictionary atom. Finally, the tampered image without CFA traces was reconstructed using the re-demosaiced dictionary. The experiments showed the effectiveness of the technique in terms of visual quality and strong CFA characteristics. Similarly, Chuang and Wu [65] also proposed two anti-forensic techniques to conceal the colour interpolation traces. These techniques used parameter perturbation and algorithm mixing to prevent identification. The results showed excellent performance to hide the CFA traces and fool the CFA identification based image forensics without loss of image quality. 2.7 Anti-forensics resampling Image resampling is related to geometric transformation. In image forensics, to make the tampering more realistic, the forged regions within the tampered image may undergo geometric transformations like scaling, rotation etc. During this phase, resampling is conducted followed by the interpolation. With regular sampling, the interpolation weight sequences are periodical, and thus they can be used to detect the resampling operation. In the literature, various techniques exist for resampling detection [66-69]. To hide the traces of resampling, different approaches have been proposed [70, 71]. Kirchner and Böhme [71], proposed an efficient AFT to hide the traces of periodic artefacts left due to the interpolation operation. The results showed good performance against the detection of resampling proposed in [66-69]. 2.8 Anti-forensics for compression Images are often compressed for effective storage and transmission. Among the compression techniques that are most popularly used are Joint Photographic Experts Group (JPEG) and JPEG 2000, which use discrete cosine transform (DCT) and discrete wavelet transform (DWT)-based transformation, respectively. To detect this type of image manipulation, image forensic tools benefit from the fact it is lossy compression. Coding fingerprints due to the compression are widely used in image forensics to detect the tampering operation. Single and double JPEG compression detection [72, 73] or other classes of image compression including the JPEG image forensics based on DCT/DWT coefficient quantisation fingerprints [74], blocking artefacts [75], and wavelet-based techniques [76], are important digital image forensic techniques used for detecting the presence of tampering in digital images. To fool these tools, several approaches have been proposed to remove these traces. A summary of several methods is shown in Table 2. Table 2. Summary of JPEG AFTs in chronological order Reference Year Target Experimental setting Remarks Stamm et al. [77] 2010 quantisation UCID [78] (244 greyscale images, JPEG compression with quality factor (QF): 90, 70, 50) add anti-forensic dither to hide quantisation artefacts, success rate: 95.9% (90 QF), 92.6%(70 QF), 81.56% (50 QF) Stamm and Liu [79] 2010 quantisation UCID [78] (244 greyscale images, SPIHT at 2bpp (bits/pixel)) add anti-forensic dither to the wavelet coefficients of highly textured blocks of the compressed image; 100% success rate Stamm and Liu [80] 2011 Quantisation & Blocking UCID [78] (1338 greyscale images, 1st experiment: JPEG compression with QF: 90, 70, 50; 2nd experiment: SPIHT at 2bpp) Add anti-forensic dither to hide quantisation fingerprints followed by deblocking to remove traces of blocking artefacts, able to defeat existing compression and history-based forensic detectors (e.g. JPEG and SPIHT compression fingerprints, double compression, image origin falsification, and cut-and-paste image forgery); success rate: 100% for JPEG and 98.2% for SPIHT Sutthiwan and Shi [81] 2011 Quantisation UCID [78] (1138 for training, 200 for testing, 20 runs, (QF1, QF2) {50, 60, 70, 80, 90}) Based on Shrink and Zoom (SAZ), mislead double JPEG compression detectors, scaling factor = 0.9 to keep a comprise between image quality and true positive rates after attack, close to 100% success in most cases Jiang et al. [82] 2013 quantisation & blocking UCID [78](1338 greyscale images, QF {55,65, 75, 85, 95}) eliminate blocking artefacts with low computational complexity by predicting the added noise, detection accuracy over Fan et al. [83] 2013 Blocking UCID [78] (1338 greyscale images of size , QF [30, 84] removes traces of JPEG compression in both spatial and transform domain, uses a two-round TV-based deblocking and histogram smoothing operation, better tradeoff between image quality and hiding compression fingerprints, PSNR improvement by 4.2 dB Barni et al. [85] 2014 quantisation created 64 histograms of DCT coefficients for each image of 2000 greyscale uncompressed images and compressed with multiple QF, 25 testing images from multiple sources universal AFT capable of misleading multiple-JPEG compression detectors. DCT sub-band histograms of a multiple-compressed image are remapped like those of a single compressed image, tested single, double, and triple compression, AUC = 0.52 Qian and Zhang [86] 2014 quantisation & blocking UCID [78](500 greyscale images, JPEG compression), QF {30, 50, 70, 80, 90} used optimisation to analyse noise distribution, filtered grainy noise due to dithering operation, less than 22% AF images are different from uncompressed for QF = 80, 50, 30 Cao et al. [87] 2015 quantisation UCID [78] (150 uncompressed images, JPEG compression, QF {55, 65, 75, 85, 90}, overall 750 JPEG images in the experiment Add chaotic noise dithering to quantised DCT coefficients, reduced visual quality degradation, detection accuracy = 86.31% Li and Zhou [88] 2015 quantisation & Blocking UCID [78] (1338 images, each compressed five times with quantisation parameter from 1 to 5) add dither noise in prediction error domain by prediction-direction preserving strategy with minimum distortion, 100% success rate. Afshin et al. [89] 2016 quantisation & blocking UCID [78] (1338 greyscale images, partitioned into overlapping blocks) dictionary-based approach using a sparse representation learned by using patches extracted from the given images, efficiently reduces detection accuracy except for very high compression ratios Shelke and Prasad [90] 2018 quantisation & blocking UCID [78] (1300 tiff colour images, smoothing parameters {100, 150, 200, 250, 300}) least Cuckoo search algorithm to remove detectable traces left by JPEG in both spatial and transform domains, accuracy = 97%, PSNR = 44.34 dB, MSE = 0.1789 Das [91] 2018 blocking BOSS database [92] (1000 random images in greyscale, 5 classical images, QF {50, 60, 70, 80, 90}) approximation of DCT coefficients, capable of removing any distortion introduced in JPEG, unlike other AFTs requiring separate method for each, success rate = 100% with QF Luo et al. [93] 2018 data-driven BossBase V1.01 [92], BOWS2-Original [94] (10,000 uncompressed greyscale images from each, training: 16,000, testing: 4,000 images, JPEG QF = 25, 50, 75 GAN on TensorFlow framework running on GPU NVIDIA, superior performance without degradation in image visual quality Li et al. [95] 2015 quantisation BOSS [92] (10,000 uncompressed images) AFT for double JPEG compression with same quantisation matrix by slightly modifying the DCT coefficients Singh and Singh [96] 2017 blocking UCID [78] (1338 single and double compressed images, QF ranges from 50 to 95) remove blocking artefacts in spatial and DCT domains, reduce grainy noise left by perceptual histogram smoothing in DCT, improved TV-based deblocking is performed in spatial domain, better tradeoff between visual quality and undetectability, high computational cost, minimum decision error against existing forensic detectors For instance, detectors can be tricked by mostly relative anti-forensic