Search

In contexts requiring user authentication, such as financial, legal, and administrative systems, signature verification emerges as a pivotal biometric method. Specifically, handwritten signature verification stands out prominently for document authentication. Despite the effectiveness of triplet loss similarity networks in extracting and comparing signatures with forged samples, conventional deep learning models often inadequately capture individual writing styles, resulting in suboptimal performance. Addressing this limitation, our study employs a triplet loss Siamese similarity network for offline signature verification, irrespective of the author. Through experimentation on five publicly available signature datasets—4NSigComp2012, SigComp2011, 4NSigComp2010, and BHsig260—various distance measure techniques alongside the triplet Siamese Similarity Network (tSSN) were evaluated. Our findings underscore the superiority of the tSSN approach, particularly when coupled with the Manhattan distance measure, in achieving enhanced verification accuracy, thereby demonstrating its efficacy in scenarios characterized by close signature similarity. [ABSTRACT FROM AUTHOR]

Digital tampering identification, which detects picture modification, is a significant area of image analysis studies. This area has grown with time with exceptional precision employing machine learning and deep learning-based strategies during the last five years. Synthesis and reinforcement-based learning techniques must now evolve to keep with the research. However, before doing any experimentation, a scientist must first comprehend the current state of the art in that domain. Diverse paths, associated outcomes, and analysis lay the groundwork for successful experimentation and superior results. Before starting with experiments, universal image forensics approaches must be thoroughly researched. As a result, this review of various methodologies in the field was created. Unlike previous studies that focused on picture splicing or copy-move detection, this study intends to investigate the universal type-independent strategies required to identify image tampering. The work provided analyses and evaluates several universal techniques based on resampling, compression, and inconsistency-based detection. Journals and datasets are two examples of resources beneficial to the academic community. Finally, a future reinforcement learning model is proposed. [ABSTRACT FROM AUTHOR]

Video forgery, a prevalent concern in today's digital age, involves the deliberate manipulation of video content, often carried out using sophisticated video editing software. In response to this challenge, the need for an automated approach to detect forged video footage has become increasingly pressing. Our proposed methodology addresses this need by employing a multi-faceted strategy. It begins with the classification of video frames as either originating from genuine sources or having undergone manipulation. To assess the authenticity, the Δ r ¯ s metric is applied to evaluate the coherence of frame sequences. Additionally, we've harnessed the power of machine learning, training a model on a diverse dataset, namely the VIFFD dataset. This robust machine learning approach, particularly the suggested Support Vector Machine (SVM) method, consistently achieves an impressive average accuracy of 94.4%, showcasing its potential as a dependable and effective solution for video forgery detection. In an era where the trustworthiness of video content is of paramount importance, our method emerges as a pivotal safeguard, contributing significantly to the preservation of the integrity and credibility of visual media. [ABSTRACT FROM AUTHOR]

The evaluation and classification of chemical properties in different copypaper products could significantly help address document forgery. This study analyzes the feasibility of utilizing infrared spectroscopy in conjunction with machine learning algorithms for classifying copy-paper products. A dataset comprising 140 infrared spectra of copy-paper samples was collected. The classification models employed in this study include partial least squares-discriminant analysis, support vector machine, and K-nearest neighbors. The key findings indicate that a classification model based on the use of attenuated-total-reflection infrared spectroscopy demonstrated good performance, highlighting its potential as a valuable tool in accurately classifying paper products and ensuring assisting in solving criminal cases involving document forgery. [ABSTRACT FROM AUTHOR]

Now-a-days biometric systems are useful in recognizing person’s identity but criminals change their appearance in behaviour and psychological to deceive recognition system. To overcome from this problem we are using new technique called Deep Texture Features extraction from images and then building train machine learning model using CNN (Convolution Neural Networks) algorithm. This technique refers as LBPNet or NLBPNet as this technique heavily dependent on features extraction using LBP (Local Binary Pattern) algorithm. In this project we are designing LBP Based machine learning Convolution Neural Network called LBPNET to detect fake face images. Here first we will extract LBP from images and then train LBP descriptor images with Convolution Neural Network to generate training model. Whenever we upload new test image then that test image will be applied on training model to detect whether test image contains fake image or non-fake image. Below we can see some details on LBP. Local binary patterns (LBP) is a type of visual descriptor used for classification in computer vision and is a simple yet very efficient texture operator which labels the pixels of an image by thresholding the neighborhood of each pixel and considers the result as a binary number. Due to its discriminative power and computational simplicity, LBP texture operator has become a popular approach in various applications. It can be seen as a unifying approach to the traditionally divergent statistical and structural models of texture analysis. Perhaps the most important property of the LBP operator in real-world applications is its robustness to monotonic gray-scale changes caused, for example, by illumination variations. Another important property is its computational simplicity, which makes it possible to analyze images in challenging real-time settings. [ABSTRACT FROM AUTHOR]

Today's web represents the most extensive engineered system ever created by humankind. Web security is critical to web application providers and end-users. Burp Suite is established as a state-of-the-art and fully featured set of tools for web vulnerability scanners. This paper presents a novel approach using state of the art Machine Learning algorithms applied to the Burp Suite extension. These algorithms were used to scan for: SQL injection, Cross-Site Request Forgery, and XML External Entity vulnerabilities in university web applications. The results show that the best algorithm is Long Short-Term Memory and that the targeted website is safe to use. [ABSTRACT FROM AUTHOR]

Banknote counterfeiting is a common practice worldwide. Due to the recent developments in technology, banknote imitation has become easier than before. There are different kinds of algorithms developed for the detection of counterfeit banknotes for different countries in the literature. The earlier algorithms utilized classical image processing techniques where the implementations of machine learning and deep learning algorithms appeared with the developments in the artificial intelligence field as well as the computer hardware. In this study, a novel convolutional neural networks-based deep learning algorithm has been developed that detects counterfeit Turkish Lira banknotes and their denominations using the banknote images taken under UV light. The results obtained with the proposed algorithm have been compared with the results obtained with state-of-the-art machine learning and deep learning algorithms. It is seen that the results obtained with the proposed algorithm are superior to the results obtained with the state-of-the-art machine learning and deep learning algorithms. The proposed algorithm achieved 100% accuracy on the training set and 99.95% accuracy on the test set while yielding low inference time with relatively few parameters and small file size. [ABSTRACT FROM AUTHOR]

Automatic Speaker Verification (ASV) has its benefits compared to other biometric verification methods, such as face recognition. It is convenient, low cost, and more privacy protected, so it can start being used for various practical applications. However, voice verification systems are vulnerable to unknown spoofing attacks, and need to be upgraded with the pace of forgery techniques. This paper investigates a low-cost attacking scenario in which a playback device is used to impersonate the real speaker. The replay attack only needs a recording and playback device to complete the process, so it can be one of the most widespread spoofing methods. In this paper, we explore and investigate some spectral clues in the high sampling rate recording signals, and utilize this property to effectively detect the replay attack. First, a small scale genuine-replay dataset of high sample rates are constructed using some low-cost mobile terminals; then, the signal features are investigated by comparing their spectra; machine learning models are also applied for evaluation. The experimental results verify that the high frequency spectral clue in the replay signal provides a convenient and reliable way to detect the replay attack. [ABSTRACT FROM AUTHOR]

The emergence of advanced machine learning or deep learning techniques such as autoencoders and generative adversarial networks, can generate images known as deepfakes, which astonishingly resemble the realistic images. These deepfake images are hard to distinguish from the real images and are being used unethically against famous personalities such as politicians, celebrities, and social workers. Hence, we propose a method to detect these deepfake images using a light weighted convolutional neural network (CNN). Our research is conducted with Deep Fake Detection Challenge (DFDC) full and sample datasets, where we compare the performance of our proposed model with various state-of-the-art pretrained models such as VGG-19, Xception and Inception-ResNet-v2. Furthermore, we perform the experiments with various resolutions maintaining 1:1 and 9:16 aspect ratios, which have not been explored for DFDC datasets by any other groups to date. Thus, the proposed model can flexibly accommodate various resolutions and aspect ratios, without being constrained to a specific resolution or aspect ratio for any type of image classification problem. While most of the reported research is limited to sample or preview DFDC datasets only, we have also attempted the testing on full DFDC datasets and presented the results. Contemplating the fact that the detailed results and resource analysis for various scenarios are provided in this research, the proposed deepfake detection method is anticipated to pave new avenues for deepfake detection research, that engages with DFDC datasets. [ABSTRACT FROM AUTHOR]

Due to the exponential growth of high-quality fake photos on social media and the Internet, it is critical to develop robust forgery detection tools. Traditional picture- and video-editing techniques include copying areas of the image, referred to as the copy-move approach. The standard image processing methods physically search for patterns relevant to the duplicated material, restricting the usage in enormous data categorization. On the contrary, while deep learning (DL) models have exhibited improved performance, they have significant generalization concerns because of their high reliance on training datasets and the requirement for good hyperparameter selection. With this in mind, this article provides an automated deep learning-based fusion model for detecting and localizing copy-move forgeries (DLFM-CMDFC). The proposed DLFM-CMDFC technique combines models of generative adversarial networks (GANs) and densely connected networks (DenseNets). The two outputs are combined in the DLFM-CMDFC technique to create a layer for encoding the input vectors with the initial layer of an extreme learning machine (ELM) classifier. Additionally, the ELM model's weight and bias values are optimally adjusted using the artificial fish swarm algorithm (AFSA). The networks' outputs are supplied into the merger unit as input. Finally, a faked image is used to identify the difference between the input and target areas. Two benchmark datasets are used to validate the proposed model's performance. The experimental results established the proposed model's superiority over recently developed approaches. [ABSTRACT FROM AUTHOR]

Hyperspectral imaging has shown tremendous growth over the past three decades. Hyperspectral imaging was evolved through remote sensing. Along, with the technological enhancements hyperspectral imaging has outgrown, conquering over other various application areas. In addition to it, data enriched data cubes with abundant spectral and spatial information works as perk for capturing, analyzing, reviewing, and interpreting results from data. This review concentrates on emerging application areas of hyperspectral imaging. Emerging application areas are selected in ways where there is a vast scope for future enhancements by exploiting cutting edge technology, that is, deep learning. Applications of hyperspectral imaging techniques in some selected areas (remote sensing, document forgery, history and archaeology conservation, surveillance and security, machine vision for fruit quality inspection, medical imaging) are focused. The review pivots around the publicly available datasets and features used domain wise. This review can act as a baseline for deep learning and machine vision experts, historical geographers, and scholars by providing them a view of how hyperspectral imaging is implemented in multiple domains along with future research prospects. This article is categorized under:Technologies > Machine LearningTechnologies > Prediction [ABSTRACT FROM AUTHOR]

In digital images, Copy-Move Forgery is a general kind of forgery techniques. The process of replicating one part of the image within the same image is termed as copy-move forgery. An effective and reliable approach needs to be developed for identifying these forgeries for restoring the image trustworthiness. The main intent of this paper is to sort out the diverse copy-move image forgery detection models. This survey makes an effective literature analysis on a set of literal works from the past 10 years. The analysis is focused on categorizing the models based on transformation models, machine learning algorithms, and other advanced techniques. The main contribution and limitations of the works are clearly pointed out. In addition, the types of datasets and the simulation platforms utilized by different copy-move forgery detection (CMFD) models are analyzed. The performance measures evaluated by different contributions have been observed for making a concluding decision. The utilization of optimization algorithms on copy-move image forgery detection has also been studied. Finally, the research gaps and challenges with future direction are discussed, which is helpful for researchers in developing an efficient CMFD that could attain high performance. [ABSTRACT FROM AUTHOR]

Recently, on the internet, the level of image and video forgery has augmented due to the augmentation in the malware, which has facilitated user (anyone) to upload, download, or share objects online comprising audio, images, or video. Recently, Convolution Neural Network (CNN) has turn into a de-facto technique for classification of multi-dimensional data and it renders standard and also highly effectual network layer arrangements. But these architectures are limited by the speed due to massive number of calculations needed for training in addition to testing the network and also, it might render less accuracy. To trounce these issues, this paper proposed to ameliorate the image and video forgery detection's efficiency utilizing hybrid CNN. Initially, the intensive along with incremental learning phase is carried out. After that, the hybrid CNN is implemented to detect the image together with video forgery. The developed system was tested on images together with videos for different kinds of forgeries, and it was observed that the proposed work obtains more than 98% accuracy for both testing as well as validation sets. [ABSTRACT FROM AUTHOR]

Protecting information from manipulation is important challenge in current days. Digital images are one of the most popular information representation. Images could be used in several fields such as military, social media, security purposes, intelligence fields, evidences in courts, and newspapers. Digital image forgeries mean adding unusual patterns to the original images that cause a heterogeneity manner in form of image properties. Copy move forgery is one of the hardest types of image forgeries to be detected. It is happened by duplicating part or section of the image then adding again in the image itself but in another location. Forgery detection algorithms are used in image security when the original content is not available. This paper illustrates a new approach for Copy Move Forgery Detection (CMFD) built basically on deep learning. The proposed model is depending on applying (Convolution Neural Network) CNN in addition to Convolutional Long Short-Term Memory (CovLSTM) networks. This method extracts image features by a sequence number of Convolutions (CNVs) layers, ConvLSTM layers, and pooling layers then matching features and detecting copy move forgery. This model had been applied to four aboveboard available databases: MICC-F220, MICC-F2000, MICC-F600, and SATs-130. Moreover, datasets have been combined to build new datasets for all purposes of generalization testing and coping with an over-fitting problem. In addition, the results of applying ConvLSTM model only have been added to show the differences in performance between using hybrid ConvLSTM and CNN compared with using CNN only. The proposed algorithm, when using number of epoch's equal 100, gives high accuracy reached to 100% for some datasets with lowest Testing Time (TT) time nearly 1 second for some datasets when compared with the different previous algorithms. [ABSTRACT FROM AUTHOR]

With the advent of so many YouTubers, the number of videos uploaded to the cloud is increasing at an exponential rate. Most of the video creators take advantage of the fact, that the videos uploaded are not checked for any kind of tampering, which is due to the computational and information limit over the cloud. Due to this, there is lot of fake-like videos over the internet, which misleads the viewers into believing whatever the video creator conveys. All this leads to a lot of chaos and misunderstanding for the viewers at large. To overcome this issue, this text proposes a novel machine learning layer which is computationally efficient and uses a two-layer processing scheme for forgery detection in large scale videos. With the help of this scheme, the system pre-learns the forgery types, and keeps a large learning set ready for speedy reference, this initially static set, is then dynamically modified based on the real time videos given to the system. This two-layer scheme allows for an improved end-to-end delay for the system, without compromising on the system security. Our evaluations on the REWIND dataset show that the proposed system gives more than 95% accuracy in terms of forgery detection, and reduces the delay by more than 50% when compared to other state-of-the art algorithms like key point selection, genetic algorithm and others. [ABSTRACT FROM AUTHOR]

Handwriting recognition is a very active research in the machine learning community. In this paper, we tackled two important applications: handwritten digit recognition and Signature verification using convolution neural network (CNN). Signature is one of the most popular personal attributes for authentication. It is basic, shabby and adequate to individuals, official associations and courts. This paper focuses on offline signature verification (SV). It is a kind of a classification problem, which classifies the signature as genuine, or forgery. We use CNN in two types of datasets: the MNIST database, and UTSIG database. In order to obtain better accuracy, we propose to preprocess the data in the wavelet domain and in the Gabor filter combining the outputs of both CNN. This combination resulted in higher recognition accuracy compared to other techniques. [ABSTRACT FROM AUTHOR]

Document examination is a vital mission for revealing illegal modifications that assist in the detection and resolution of criminal acts. Addition and alteration are more frequently used in handwritten documents. However, most of the documents have been modified with similar inks, and it is tough to detect or observe them with human eyes. As a result, there is a need for methods to automatically detect handwriting forgery to reach an accurate detection efficiently. In this paper, a novel and efficient method is proposed for automatically detecting altered handwritten documents and locating the fake part. Therefore, DE-Net is proposed to identify the forged document using a digitally scanned version of the document. Unlike the existing methods, a further localization schema is applied to locate the forged parts in the candidate forged document accurately. Where each forged document is segmented into objects. Color histograms of R, G, and B channels are used to generate a fused feature vector for each object. Then a structural similarity index (SSIM) is applied to detect the lower similarity parts as forged. The experimental results demonstrate that the proposed method can identify and localize foreign ink in handwritten documents with high performance. • Propose a novel and efficient method for exposing altered documents via DE-Net. • Localize the foreign ink based on image processing rather than analytical chemistry. • SSIM of RGB color histograms is used as handcrafted features for localization. • An integrated dataset is introduced for document forensics and machine learning use. [ABSTRACT FROM AUTHOR]

Nowadays, various image editing tools are available that can be utilized for manipulating the original images; here copy-move forgery is most common forgery. In copy-move forgery, some part of the original image is copied and pasted into the same image at some other location. However, Artificial Intelligence (AI) based approaches can extract manipulated features easily. In this study, a deep learning-based method is proposed to classify the copy-move forged images. For classifying the forged images, a deep learning (DL) based hybrid model is presented named as VI-NET using fusion of two DL architectures, i.e., VGG16 and Inception V3. Further, output of two models is concatenated and connected with two additional convolutional layers. Cross-validation protocols, K 10 (90 % training, 10 % testing), K5 (80 % training, 20 % testing), and K2 (50 % training, 50 % testing) are applied on the COMOFOD dataset. Moreover, the performance of VI-NET is compared with transfer learning and machine learning models using evaluation metrics such as accuracy, precision, recall, F1 score, etc. Proposed hybrid model performed better than other approaches with classification accuracy of 99 ± 0.2 % in comparison to accuracy of 95 ± 4 % (Inception V3), 93 ± 5 % (MobileNet) , 59 ± 8 % (VGG16) , 60 ± 1 % (Decision tree) , 87 ± 1 % (KNN), 54 ± 1 % (Naïve Bayes) and 65 ± 1 % (random forest) under K 10 protocol. Similarly, results are evaluated based on K2 and K5 validation protocols. It is experimentally observed that the proposed model performance is better than existing standard and customized deep learning architectures. [ABSTRACT FROM AUTHOR]

Document forgery is a significant problem for ages due to paper-based documents' pervasive use. Classical destructive approaches for this problem, such as chromatography and electrophoresis, cannot be implemented as they flaw the document under analysis. Hyperspectral imaging - non-destructive approach that assists in finding the unique features of an image under investigation through their unique spectral signatures. It captures multiple narrow-band images at the electromagnetic spectrum, which is difficult through conventional imaging. Deep learning approaches for hyperspectral images have attained state-of-the-art results for solving many complex and challenging problems. Supervised classification of hyperspectral images is a tedious task since obtaining image labels and labeling the training data is a time-consuming and expensive process. In this paper, an unsupervised approach for classification of hyperspectral document images is proposed. To propose an unsupervised deep learning approach for ink mismatch detection in hyperspectral document images using spectral features. CAE-LR approach is proposed that uses Convolutional Autoencoder (CAE) for feature extraction and utilizing them for ink mismatch detection through Logistic Regression (LR). We evaluated the performance of CAE-LR on UWA writing ink hyperspectral images dataset for blue and black inks. Artificially similar color inks of different types (2∼5) were mixed in varying proportions to detect ink mismatch. Additionally, results are compared with three machine learning algorithms with variants of each, CNN, and five state-of-art methods used by the researchers. Experimental results illustrated that the CAE-LR outperforms all the above – mentioned approaches by achieving the state of art results, which depicts the efficacy of unsupervised deep learning approach for ink mismatch detection in hyperspectral document images. [ABSTRACT FROM AUTHOR]

• A multi-semantic CRF-based attention model is proposed to characterize the local patter correlations among neighboring pixels. • The proposed attention model is used to identify the boundary of forged region, guiding the network to capture more intrinsic fingerprint for better generalization capability. • A deep learning based image forgery detection and localization scheme is developed, which shows superior generalization ability over the competing methods. In this paper, a novel image forgery detection and localization scheme is proposed based on deep convolutional neural network (CNN) which incorporates multi-semantic CRF-based attention model. The proposed method is based on the key observation that the boundary transition artifacts arising from the blending operations are ubiquitous in various image forgery manipulations, which is well characterized in our method with CRF (conditional random field) based attention model by generating attention maps to represent the probability of being forged for each pixel in the image. The resulting attention maps are then used to re-weight the convolutional feature maps for noise suppression and highlighting the informative regions surrounding the forged boundaries, guiding the network to capture more intrinsic features for image forgery rather than manipulation-specific artifacts. Multi-scale attention maps with various semantics are adopted to take full advantages of both the local and global information for improvement of generalization capability, which is then incorporated with a CNN model for effective image forgery detection and localization. Extensive experimental results on several public datasets show that the proposed scheme outperforms or rivals to other state-of-the-art methods in image forgery detection and localization. [ABSTRACT FROM AUTHOR]

Today, embedded systems are augmented with the Internet of things and more with the artificial intelligence to make world even connected with aliens. With an IoT networks are getting its insight since it deals with large number of data information, security has considered to be more important and needs to be a diagnosis for every minute. To enhance the security in the network, a mathematically secure algorithms were formulated and runs on the cryptographic embedded chips to counterfeit the risks which are caused by the different attacks such as side channel attacks (SCA) on the networks. Even though many cryptographic encryption algorithms such as AES, DES, RC4 algorithms were gaining its importance, fixed encryption keys, non-intelligent detection of attacks, cognitive countermeasures are some of the real-time challenges in an existing system of encryption. Following the limitations of existing systems, this research article focuses on design of new AES with HPAC-SBOX (Hybrid Prediction and Adaptive Chaos) which integrates powerful predictive learning algorithms and adaptive chaotic logistic S-Box. The following contributions of this research articles are: a) Preparation of Data Sets from the Power consumption traces captured from Multi Core Embedded boards while running the Advanced Encryption Systems(AES) on it b) Implementation of High Speed and High Accurate Prediction learning machines for the prediction of side-channel attacks c) Design of Adaptive Chaotic S-Box using 3-Dlogistic Hyperbolic maps for attacked bits. To evaluate the proposed architecture, experimentation in carried out in an IoT networks and various performance parameters were calculated and analyzed. The results show that the proposed architecture outperforms the other existing algorithms in terms of prediction and performance. [ABSTRACT FROM AUTHOR]

The problem of forged images has become a global phenomenon that is spreading mainly through social media. New technologies have provided both the means and the support for this phenomenon, but they are also enabling a targeted response to overcome it. Deep convolution learning algorithms are one such solution. These have been shown to be highly effective in dealing with image forgery derived from generative adversarial networks (GANs). In this type of algorithm, the image is altered such that it appears identical to the original image and is nearly undetectable to the unaided human eye as a forgery. The present paper investigates copy-move forgery detection using a fusion processing model comprising a deep convolutional model and an adversarial model. Four datasets are used. Our results indicate a significantly high detection accuracy performance (~95%) exhibited by the deep learning CNN and discriminator forgery detectors. Consequently, an end-to-end trainable deep neural network approach to forgery detection appears to be the optimal strategy. The network is developed based on two-branch architecture and a fusion module. The two branches are used to localize and identify copy-move forgery regions through CNN and GAN. [ABSTRACT FROM AUTHOR]

We investigated machine learning-based joint banknote recognition and counterfeit detection method. Unlike existing methods, since the proposed method simultaneously recognize banknote type and detect counterfeit detection, it is significantly faster than existing serial banknote recognition and counterfeit detection methods. Furthermore, we propose an explainable artificial intelligence method for visualizing regions that contributed to the recognition and detection. Using the visualization, it is possible to understand the behavior of the trained machine learning system. In experiments using the United State Dollar and the European Union Euro banknotes, the proposed method shows significant improvement in computation time from conventional serial method. [ABSTRACT FROM AUTHOR]