Image Forensic Research Papers - Academia.edu (original) (raw)

This paper describes the implementation of a system that addresses the issues in the area of Forensic Signature Verification. Two main approaches exist in this field-signature verification and signature identification. Our efforts focus... more

- by Sondos M. Fadl and +2
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- Image Processing, Image Forensic, Image Tampering, Image forensics

With the advent of powerful image editing tools, manipulating images and changing their content is becoming a trivial task. Now, you can add, change or delete significant information from an image, without leaving any visible signs of such tampering. With more than several millions pictures uploaded daily to the net, the move towards paperless workplaces, and the introduction of e-Government services everywhere, it is becoming important to develop robust detection methods to identify image tampering operations and validate the credibility of digital images. This led to major research efforts in image forensics for security applications with focus on image forgery detection and authentication. The study of such detection techniques is the main focus of this paper. In particular, we provide a comprehensive survey of different forgery detection techniques, complementing the limitations of existing reviews in the literature. The survey covers image copy-move forgery, splicing, forgery due to resampling, and the newly introduced class of algorithms, namely image retouching. We particularly discuss in detail the class of pixel-based techniques which are the most commonly used approaches, as these do not require any a priori information about the type of tampering. The paper can be seen as a major attempt to provide an up-to-date overview of the research work carried in this all-important field of multimedia.

- by Prof. Dr. Muhammad Ali Qureshi
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- Bibliography, Image Forensic, Survey, Image inpainting

Laporan Hasil Analisis Gambar Menggunakan Aplikasi Web Fotoforensics dan Forensicallybeta Serta Laporan Hasil Analisis Gambar Menggunakan Metode ORB dan SHIFT

Powerful image editing tools like Adobe Photoshop etc. are very common these days. However due to such tools tampering of images has become very easy. Such tampering with digital images is known as image forgery. The most common type of digital image forgery is known as copy move forgery wherein a part of image is cut/copied and pasted in another area of the same image. The aim behind this type of forgery may be to hide some particularly important details in the image. A method has been proposed to detect copy-move forgery in images. We have developed an algorithm of image-tamper detection based on the Discrete Wavelet Transform i.e. DWT. DWT is used for dimension reduction, which in turn increases the accuracy of results. First DWT is applied on a given image to decompose it into four parts LL, LH, HL, and HH. Since LL part contains most of the information, SIFT is applied on LL part only to extract the key features and find descriptor vector of these key features and then find similarities between various descriptor vectors to conclude that the given image is forged. This method allows us to detect whether image forgery has occurred or not and also localizes the forgery i.e. it tells us visually where the copy-move forgery has occurred.

- by Aaditya Hambarde (Ady) and +1
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- Information Security, Image Forensic, Digital Image Processing, Wavelet Transforms

Studies towards image forensics are about a decade old and various forms of research techniques have been presented till date towards image forgery detection. Majority of the existing techniques deals with identification of tampered regions using different forms of research methodologies. However, it is still an open-end question about the effectiveness of existing image forgery detection techniques as there is no reported benchmarked outcome till date about it. Therefore, the present manuscript discusses about the most frequently addressed image attacks e.g. image splicing and copy-move attack and elaborates the existing techniques presented by research community to resist it. The paper also contributes to explore the direction of present
research trend with respect to tool adoption, database adoption, and
technique adoption, and frequently used attack scenario. Finally, significant open research gap are explored after reviewing effectiveness of existing techniques.

Abstrak Pesatnya perkembangan teknologi informasi memungkinkan pengguna untuk melakukan pengolahan citra digital dengan menggunakan software editing tools yang banyak tersedia saat ini dengan sangat mudah. Pengolahan citra digital yang mudah ini tak jarang disalahgunakan oleh segelintir orang untuk melakukan kejahatan seperti pemalsuan citra untuk dijadikan hoax dan disebarluaskan. Permasalahan tersebut membuat penulis terdorong untuk menganalisa keaslian sebuah citra digital dengan menggunakan tools image forensic berupa FotoForensic, Ghiro, Forensically, dan Jpegsnoop. Teknik yang digunakan penulis adalah dengan teknik Error Level Analysis (ELA), Metadata, dan JPEG Compression. Kata Kunci : Citra digital, error level analysis, metadata, JPEG Compression Abstarct The rapid development of information technology using digital image processing with software editing tools that are widely available today is very easy to find. This easy digital image processing is not infrequently done by a handful of people to commit crimes such as falsification of images to be deceived and disseminated. These problems make the writer compelled to analyze digital images by using forensic drawing tools such as FotoForensic, Ghiro, Forensically, and Jpegsnoop. The technique used by the author is the Error Level Analysis (ELA), Metadata, and JPEG Compression techniques.

- by I Gede Bagati Kusuma
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- Image Forensic, Digital Image Forensic, IT Forensics, Citra Digital

Due to the ease of access to platforms that can be used by forgers to tamper digital documents, providing automatic tools for identifying forged images is now a hot research field in image processing. This paper presents a novel forgery detection algorithm based on variants of Benford's law. In the proposed method, Mean Absolute Deviation (MAD) feature is extracted using traditional Benford's law. Also, generalized Benford's law is used for mantissa distribution feature vector. In addition to Benford's law-based features, other statistical features are used to construct the final feature vector. Finally, support vector machine (SVM) with three different kernel functions is used to classify original and forged images. The method has been tested on two common image datasets (CASIA V1.0 and V2.0). The experimental results show that 0.27% and 0.21% improvements on CASIA V1.0 and CASIA V2.0 datasets were achieved, respectively in detection accuracy by the proposed method in comparison to best state-of-the-art methods. The proposed efficient algorithm has a simple implementation. Moreover, on the basis of Benford's law rich features are extracted from images so that classification process is efficiently performed by a simple SVM classifier in a short time.

- by arman parnak
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- Electronic Engineering, Artificial Intelligence, Image Processing, Machine Learning

The use of machine-learning for multimedia forensics is gaining more and
more consensus, especially due to the amazing possibilities offered by
modern machine learning techniques. By exploiting deep learning tools, new approaches have been proposed whose performance remarkably exceed those achieved by state-of-the-art methods based on standard machine-learning and model-based techniques. However, the inherent vulnerability and fragility of machine learning architectures pose new serious security threats, hindering the use of these tools in security-oriented applications, and, among them, multimedia forensics. The analysis of the security of machine learning-based techniques in the presence of an adversary attempting to impede the forensic analysis, and the development of new solutions capable to improve the security of such techniques is then of primary importance, and, recently, has marked the birth of a new discipline, named Adversarial Machine Learning. By focusing on Image Forensics and image manipulation detection in particular, this thesis contributes to the above mission by developing novel techniques for enhancing the security of binary manipulation detectors based on
machine learning in several adversarial scenarios. The validity of the proposed solutions has been assessed by considering several manipulation tasks, ranging from the detection of double compression and contrast adjustment, to the detection of geometric transformations and filtering operations.

In the modern era of digital publishing and imaging, many a time, images are retouched and manipulated to increase and enhance the aesthetic beauty of the images. Several images are morphed before publishing in order to incorporate extra information. The problem is altogether intensified by the presence of various methods of image capture. There exist a variety of cameras with different resolutions and encoding. Due to the availability of image editing tools, tampering with images has become relatively easy. Many times the forged image is compressed and resized before publishing. Hence detecting image forgery in such cases is a challenging task. Different methods of forgery are resizing, blurring, scaling, rotation, addition of noise to the image, addition of some vital data to the image or removal of some data from the image etc. However most methods attempt to detect a particular type of image forgery. In this paper comprehensive technique has been presented for detection of any type of image forgery. Feature extraction techniques like DCT, LBP, Curvelet transform, Gabor filter etc. are used to represent the image in transformed domain. HMM and SVM are the machine learning methods used to classify the image into either of the two classes (Authentic or Forged). CASIA image database was used for training and testing the system.

- by Aaditya Hambarde (Ady) and +1
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- Artificial Intelligence, Image Processing, Information Security, Image Forensic

Rapid penetration of internet and advancements in
communication technology is a paved way to easy access of
digital images. Nevertheless, these advancements also create
ways for malicious users to pirate and sell the copyrighted
content. Digital watermarking techniques have been deployed
in combating the piracy issue. The current digital
watermarking methods are facing the problems in maintaining
invisibleness, robustness, capacity and security. In this article,
authors are analyzing the various popular algorithms used in
digital watermark of copyright protection and propose a novel
approach by combining three such algorithms aiming to
increase the invisibleness and robustness of the watermarked
image. The experimental results, exemplifies much increase in
invisibleness and a nominal increase in robustness.

- by Dr Vaishnavi D
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- Image Processing, Image Forensic, Image Watermarking

Image forgery is a major issue today in publishing and printing. Several images are morphed before publishing in order to incorporate extra information. The problem becomes more complicated with different means of image capture. There exist a variety of cameras with different resolutions and encoding techniques. Many times the forged image is compressed or resized before publishing. Detecting forgery in such cases is a challenging task. Different techniques of tampering include resizing, blurring, compression, addition of noise, image splicing etc. The most common type of digital image forgery is copy-move forgery. In this paper, a passive technique for detecting copy-move forgery based on wavelet transforms and SIFT features is proposed. The wavelet transforms employed are Discrete Wavelet Transform (DWT) and Dyadic Wavelet Transform (DyWT). The image is divided into four sub-bands viz. LL, LH, HL and HH by the wavelet transform. Since the LL sub-band contains most of the information, SIFT is applied on the LL part only, to extract the key features and find descriptor vector of these key features and then find similarities between various descriptor vectors to conclude that the given image is forged.

- by Aaditya Hambarde (Ady) and +1
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- Image Processing, Information Security, Image Forensic, Information Security and Privacy
- by DrNoura Semary and +2
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- Image Processing, Image Forensic, Image Forgery Detection, Digital Image Processing

Last-generation GAN models allow to generate synthetic images which are visually indistinguishable from natural ones, raising the need to develop tools to distinguish fake and natural images thus contributing to preserve the trustworthiness of digital images. While modern GAN models can generate very high-quality images with no visibile spatial artifacts, reconstruction of consistent relationships among colour channels is expectedly more difficult. In this paper, we propose a method for distinguishing GAN-generated from natural images by exploiting inconsistencies among spectral bands, with specific focus on the generation of synthetic face images. Specifically, we use cross-band co-occurrence matrices, in addition to spatial co-occurrence matrices, as input to a CNN model, which is trained to distinguish between real and synthetic faces. The results of our experiments confirm the goodness of our approach which outperforms a similar detection technique based on intra-band spatial co-occurrences only. The performance gain is particularly significant with regard to robustness against post-processing, like geometric transformations, filtering and contrast manipulations.

- by Ehsan Nowroozi
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- Image Processing, Machine Learning, Image Forensic, Image Forgery Detection

Copy–move forgery is one of the most common types of tampering for digital images. Detection methods
generally use block-matching approaches, which first divide the image into overlapping blocks and then extract and compare features to find similar ones, or point-based approaches, in which relevant keypoints are extracted and matched to each other to find similar areas. In this paper, we present a very novel hybrid approach, which compares triangles rather than blocks, or single points. Interest points are extracted from the image, and objects are modeled as a set of connected triangles built onto these points. Triangles are matched according to their shapes (inner angles), their content (color information), and the local feature vectors extracted onto the vertices of the triangles. Our methods are designed to be robust to geometric
transformations. Results are compared with a state-of-the-art block matching method and a point-based method. Furthermore, our data set is available for use by academic researchers.

Introductory material to the online course "Digital Video Forensics: Uncovering the Truth in a World of Distorted Realities"

- by arman parnak
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- Engineering, Electronic Engineering, Artificial Intelligence, Image Processing
- by Noura Semary
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- Computer Science, Forensics, Image Processing, Image Forensic
- by B. Mahdian and +1
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- Digital Investigation, Image Forensic, Image Forgery Detection, Time Complexity
- by Pankaj Jindal
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- Image Forensic, Image Forgery Detection, Image manipulations

- by Rachel Tinoco
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- Forensic Anthropology, Photography, Brazil, Face
- by Ghulam Siddiqui
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- Engineering, Computer Science, Image Processing, Forensic Science

Data and information security technique is a way to keep an object confidential. eXchangable Image File Format (EXIF) Metadata is data information about digital image. In the digital forensic investigation, this is very useful for copyright security or safeguarding digital evidence. Steganography and cryptography techniques have been widely used to overcome these problem. However, from the research that has been done in terms of security EXIF of metadata digital image photography, there are still some weaknesses. Among them is the depreciation of the digital image size after decryption. Since the standard EXIF metadata treatmens released by JEIDA and CIPA have different characteristic with JFIF metadata. A null value will be deemed to be the content with a value of 1 in EXIF standard. While in JFIF metadata model, the value of null is considered to be worth 0 (zero). This is make the EXIF metadata security process is shrinking of-25,15% from the original size. With the optimizing null value before encryption, the same metadata will be obtained between original image and after decryption. The null value will be replaced with an empty string (it's worth 1) before encryption. Results from the model obtained an average of-1,51% depreciation of the original digital image size.Therefore, this technique can optimize the digital image copyright protection. I. PENDAHULUAN Citra digital merupakan salah satu jenis barang bukti digital yang memiliki tingkat resiko manipulasi dan kehilangan informasi yang sangat tinggi. Data citra digital (metadata) memiliki informasi yang sangat kompleks, meliputi informasi perangkat pengambilan gambar sampai dengan waktu kapan diambil (Zhou et al., 2016). Sehingga dapat dikatakan metadata citra digital tersebut dapat digunakan sebagai hak cipta. Dalam ranah digital forensik, selain konten citra, metadata dapat digunakan sebagai barang bukti digital di persidangan. Mudahnya manipulasi dari sisi metadata dan konten citra inilah yang menjadi celah untuk melemahkan kedudukan citra digital sebagai alat bukti di persidangan. Banyak sekali cara untuk mengamankan metadata, mulai dari teknik steganografi, kriptografi sampai dengan watermarking. Kombinasi pengamanan eXchangable Image File Format (EXIF) metadata dengan teknik steganografi dan kriptografi juga telah dilakukan penelitian dengan hasil hampir sempurna dalam mengamankan metadata citra digital. Hal ini disebabkan antara citra digital asli, citra digital setelah dienkripsi, dan citra digital setelah dilakukan dekripsi, memiliki nilai histogram sama, serta dapat mengamankan keseluruhan metadata. Akan tetapi dalam penelitian tersebut, ukuran citra digital mengalami penyusutan sebesar-25.15%, sehingga hanya dapat digunakan sebagai perlindungan hak cipta citra digital saja. Untuk dapat digunakan sebagai perlindungan barang bukti digital, nilai hashing citra asli dan citra setelah dekripsi harus sama (Wijayanto et al., 2016). Penyusutan dalam penelitian tersebut dikarenakan perbedaan tipe header metadata citra asli dengan citra setelah dekripsi, yaitu eXchangable Image File Format (EXIF) menjadi JPEG File Interchange Format (JFIF) (Wijayanto et al., 2016). Ini disebabkan

- by Hendro Wijayanto
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- Computer Science, Information Security, Image Forensic, Digital Forensics

Image forgery detection is currently one of the interested research fields of image processing. Copy-Move (CM) forgery is one of the most commonly techniques. In this paper, we propose an efficient methodology for fast CM forgery detection. The proposed method accelerates blocking matching strategy. Firstly, the image is divided into fixed-size overlapping blocks then Discrete Cosine Transform (DCT) is applied to each block to represent its features, which are used to indirectly compare the blocks. After sorting the
blocks based on DCT coefficients, a distance is measured between nearby blocks to denote their similarity. The proposed Fan Search (FS) algorithm starts once a duplicated block is detected. Instead of exhaustive search for all blocks,
the nearby blocks of the detected block are examined first in a spiral order. The experimental results demonstrate that the proposed method can detect the duplicated regions efficiently, and reduce processing time up to 75% less than
other previous works.

- by DrNoura Semary and +1
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- Image Processing, Image Forensic, Image Tampering, Image forensics
- by Mohammad Akbarpour Sekeh
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- Image Forensic, Image Forgery Detection, Copy Move Forgery

- by Marco Aurelio Guimarães
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- Forensic Anthropology, Photography, Brazil, Face
- by Mohd Foad Rohani
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- Digital Investigation, Image Forensic, Image Forgery Detection, Time Complexity
- by Benedetta Tondi
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- Engineering, Computer Science, Machine Learning, Image Forensic

We investigate if the random feature selection approach proposed in [1] to improve the robustness of forensic detectors to targeted attacks , can be extended to detectors based on deep learning features. In particular, we study the transferability of adversarial examples targeting an original CNN image manipulation detector to other detectors (a fully connected neural network and a linear SVM) that rely on a random subset of the features extracted from the flatten layer of the original network. The results we got by considering three image manipulation detection tasks (resizing, median filtering and adap-tive histogram equalization), two original network architectures and three classes of attacks, show that feature randomization helps to hinder attack transferability, even if, in some cases, simply changing the architecture of the detector, or even retraining the detector is enough to prevent the transferability of the attacks.

- by Ehsan Nowroozi
- •
- Machine Learning, Image Forensic, Deep Learning, Support Vector Machines (SVMs)

The study of facial growth is explored in many fields of science, including anatomy, genetics, and forensics. In the field of forensics, it acts as a valuable tool for combating child pornography. The present research proposes a new method, based on relative measurements and fixed references of the human face—specifically considering measurements of the diameter of the iris (iris ratio)—for the analysis of facial growth in association with age in children and sub-adults. The experimental sample consisted of digital photographs of 1000 Brazilian subjects , aged between 6 and 22 years, distributed equally by sex and divided into five specific age groups (6, 10, 14, 18, and 22 year olds ± one month). The software package SAFF-2D ® (Forensic Facial Analysis System, Brazilian Federal Police, Brazil) was used for positioning 11 landmarks on the images. Ten measurements were calculated and used as fixed references to evaluate the growth of the other measurements for each age group, as well the accumulated growth (6–22 years old). The Intraclass Correlation Coefficient (ICC) was applied for the evaluation of intra-examiner and inter-examiner reliability within a specific set of images. Pearson's Correlation Coefficient was used to assess the association between each measurement taken and the respective age groups. ANOVA and Post-hoc Tukey tests were used to search for statistical differences between the age groups. The outcomes indicated that facial structures grow with different timing in children and adolescents. Moreover, the growth allometry expressed in this study may be used to understand what structures have more or less proportional variation in function for the age ranges studied. The diameter of the iris was found to be the most stable measurement compared to the others and represented the best cephalometric measurement as a fixed reference for facial growth ratios (or indices). The method described shows promising potential for forensic applications, especially as part of the armamentarium against crimes involving child pornography and child abuse.

- by Martin Evison and +1
- •
- Forensic Anthropology, Image Forensic, Forensic Facial Identification

Apart from robustness and accuracy of copy–paste image forgery detection, time complexity also plays an important role to evaluate the performance of the system. In this paper, the focus point is to improve time complexity of the block-matching algorithm. Hence, a coarse-to-fine approach is applied to propose an enhanced duplicated region detection model by using sequential block clustering. Clustering minimizes the search space in block matching. This significantly improves time complexity as it eliminates several extra block-comparing operations. We determine time complexity function of the proposed algorithm to measure the performance. The experimental results and mathematical analysis demonstrate that our proposed algorithm has more improvement in time complexity when the block size is small.

- by Mohd Foad Rohani
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- Digital Investigation, Image Forensic, Image Forgery Detection, Time Complexity
- by Sondos Magdy
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- Forensics, Image Processing, Image Forensic, Image Forgery Detection

- by Ademir Franco
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- Forensic Anthropology, Photography, Brazil, Face

Copy move (CM) is a common type of forgery which creates tampered image by covering some important part of the image by replacing it with some other part of the same image. Therefore, forgery detection techniques are required to identify tampered areas. In this paper, a copy-move forgery detection (CMFD) method is presented by utilizing Stationary Wavelet transform (SWT) for decomposing the image. After decomposition, approximation band is further subdivided into overlapping blocks and then features are extracted by using principal component analysis (PCA) for each block. In order to match the similar block pairs and identify areas that are likely to be tampered, euclidean distance is used to compute the block distance between the adjacent pair of blocks. To evaluate the performance of the proposed work uncompressed color image dataset (UCID) is used. The quantitative results have been discussed in terms of parameters true positive rate (TPR), false positive rate (FPR) and accuracy and also compared with other existing methods. For qualitative analysis detected images obtained from the proposed method is presented. From the results obtained it can be seen that the proposed method performs better in terms of all parameters as compare to existing methods.

- by ijsetr journal
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- Image Forensic, Authentication, Copy Move Forgery Detection, Tampered images

Image forgery is nowadays widely used as digital images are easy to manipulate due to high availability of powerful image processing tools. It is possible to add or remove objects from an image without leaving any visible traces of tampering. This paper describes a method for detection of copy-paste manipulation on JPEG digital images. It is a type of image forgery in which a part of the image is copied to another location in the image with the intent to cover or add an important image object. The detection method was implemented through extracting and analyzing blocking artifact grids (BAGs), introduced by block processing during JPEG compression. Analysis was based on fact that BAGs usually mismatch after performing copy-paste operations. Proposed method was demonstrated on two doctored images.

- by Dijana Tralić
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- Image Forensic, Image Forgery Detection

Postmortem analysis of the ocular globe is an important topic for forensic pathology and transplantology. Although crucial elements may be gathered from examining cadaveric eyes, the latter do not routinely undergo in-depth analysis. The paucity of quantitative and objective data that are obtainable using current, invasive necroscopic techniques is the main reason for the limited interest in this highly specialized procedure. The aim of the current study is to describe and to object for the first time the postmortem ocular changes by mean of portable optical coherence tomography for evaluating ocular tissues postmortem. The design involved the postmortem analysis (in situ, and without enucleation) of 12 eyes by portable spectral-domain Optical Coherence Tomography. The scans were performed, in corneal, retinal and angle modality at different intervals: <6 h, 6th, 12th, and 24th hour and after autopsy (25th–72nd hour). The morphological changes in the cornea, sclera, vitreous humo...

- by Matteo Nioi
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- Ophthalmology, Image Forensic, Optical coherence tomography, Cornea
- by Noura Semary
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- Image Processing, Image Forensic, Image Tampering, Image forensics
- by Noura Semary
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- Computer Science, Forensics, Image Processing, Image Forensic

- by Benedetta Tondi
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- Engineering, Computer Science, Machine Learning, Image Forensic
- by Daniele Ravì
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- Image Forensic, Image Retrieval, Digital Image Forensic, Bag of Visual Words

Whilst it is sometimes essential that a scene is well lit before image capture, too much light can cause exposure or glare-based problems. Typically, glare is introduced to images when the camera is pointed towards the light source, and results in a visible distortion in the image. In this paper, we analyse and identify images that contain the 'glare' property using the empirical Benford's Law. The experiment is performed on 1338 images, and extracts discrete wavelet High High (HH), High Low (HL) and Low High (LH) sub bands as raw data. The significant digit from each coefficient of all sub bands is then calculated. We then analyse the probability of occurrence of large digits against smaller digits to detect anomalies. All images containing these anomalies are further analysed for the identification of additional salient features. This analysis is performed in accordance with the Benford's Law plot and the help of probability intensity histogram and divergence. Our results indicate that 142 images have irregular Benford's Law curves. For most images, the irregularity occurs at the 5th digit. After visual examination, we have found the unbalanced light and high level of brightness in these images. To measure the intensity of light in an image, we compute the probability histogram of gray levels. These results also correlate with the irregular peak identified from the Benford's Law curves. In addition, the divergence is then computed, which shows the deviation between the actual Benford's Law curve and the Benford's Law graph of an image. Our proposed technique is novel and has a potential to be an image forensic tool for quick image analysis.

- by Ghulam Siddiqui
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- Image Forensic, Anomaly Detection, Digital Image Forensic
- by Ghulam Siddiqui
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- Image Forensic
- by Sebastiano Battiato
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- Image Forensic