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image Copy-Move Forgery Detection Algorithms based on Spatial Feature Domain 17

Image Copy-Move Forgery Detection Algorithms Based on Spatia...

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17th ieee international Colloquium on signal Processing and Its Applications, CSPA 2021

作者： Ahmed, Ismail Taha Hammad, Baraa Tareq Jamil, Norziana College of Computer Sciences and Information Technology University of Anbar Anbar Iraq College of Computing and Informatics Universiti Tenaga Nasional Malaysia

ISBN: (纸本)9780738143972

Currently, digital image forgery (DIF) become more active due to the advent of powerful image processing tools. On a daily, many images are exchanged through the internet, which makes them susceptible to such effects. One of the most popular of the passive image forgery techniques is copy-move forgery. In the Copy-move forgery, the basic process is copy/paste from one area to another in the same image. In this paper, the proposed image copy-move forgery detection (IC-MFDs) involves five stages: image preprocessing, dividing the image into overlapping blocks, calculating the mean and standard deviation of each block, feature vectors are then sorted lexicographically, then feeding the feature vector to the Support Vector Machine (SVM) classifier to identify the image as authentic or forged. Experiments are performed on a standard dataset of copy move forged images MICC-F220 to evaluate the proposed technique. The findings indicate that the proposed IC-MFDs can be extremely accurate in terms of Detection Accuracy (98.44). We also compare some state-of-the-art approaches with our proposed IC-MFDs. It's noted that the findings obtained are better than these approaches. © 2021 ieee.

关键词： Statistics

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A robust digital watermarking method for textile image 6

A robust digital watermarking method for textile image

引用

6th ieee international conference on Intelligent Computing and signal Processing, ICSP 2021

作者： Yu, Ying Qiao, Yue Wang, Caiyin Li, Chao Shi, Xiaojuan Dalian Polytechnic University School of Light Industry and Chemical Engineering Dalian China

ISBN: (纸本)9780738143705

In this era of rapid development of network, the infringement of the textile industry is becoming more and more important, which seriously restricts the development of textiles. In this paper, a robust digital watermarking method based on chrominance and texture features of textile images is proposed. The cover image was transformed into CIE LAB color space, and the B channel of the cover image was extracted. The B channel of the carrier image was divided into blocks, and each image block was transformed by DCT. In order to better to maintain the visual effect of the image and the robustness of the watermark, the watermark is embedded in the intermediate frequency domain of DCT. Then the digital image is transferred to the textile by using the dark transfer film through the thermal transfer printing technology. The experimental results indicate that the method is robust to the attack of textile image in the process of print-canning. And the method could coordinate the contradiction between invisibility and robustness. © 2021 ieee.

关键词： Textiles

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A Study of Multi-user Cooperative Spectrum Sensing based on BOMP Algorithm 13

A Study of Multi-user Cooperative Spectrum Sensing Based on ...

引用

13th international conference on Wireless Communications and signal Processing, WCSP 2021

作者： Lu, Yin Fu, Mingwa Zhu, Hongbo Nanjing University of Posts and Telecommunications School of Internet of Things Nanjing China

ISBN: (纸本)9781665407854

For improving the performance of spectrum sensing, the cooperative spectrum sensing technology has attracted much attention in Cognitive Radio (CR) research area. In this paper, we focus on the decision fusion technology in the cooperative sensing under IoT application scenarios, including the estimation of the individual node sensing reliability at the fusion center, the statistics method and the relevant probability which are used to estimate the detection reliability of each node. A wide-band spectrum sensing method based on the Block Orthogonal Matching Pursuit (BOMP) algorithm is proposed according to the spectrum sparsity in wide-band radio. The influence factor \alpha\in(0,\ 1) is introduced to Analog Information Converter and Minimum Description Length (AIC and MDL) criterion for estimating the spectrum sparsity, which is taken as the stopping iteration condition for the BOMP algorithm. The simulation results show that the sensing delay is greatly reduced and the performance of sensing is improved. © 2021 ieee.

关键词： Cognitive radio

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Deepfake image Detection Using Yolov8

Deepfake Image Detection Using Yolov8

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ieee Punecon

作者： Pranav Sathe Varad Sabane Chaitanya Undale Atharva Uttarkar Vaibhav Chavhan Nilesh P. Sable Anuradha Yenkikar Department of CSE – Artificial Intelligence Vishwakarma Institute of Information Technology Pune India

ISBN: (数字)9798331527822

ISBN: (纸本)9798331527839

Due to growing number of fake media and the possible problems with misinformation and identity theft, deepfake image recognition has become a hot topic. In order to evaluate the efficacy of widely recognized deep learning models in detecting and classifying deepfake images, we compare and contrast YOLO (You Only Look Once) V8, CNN (Convolutional Neural Network), combination of LSTM (Long Short-Term Memory) with CNN and ResNet in this paper. The capacity to recognize photos that have been manipulated effectively is essential for maintaining trust regarding digital media and preventing the spread of deceptive data. The objective of our research is to explain each model's abilities, limitations and performance in the context of deepfake image recognition. We evaluated each model's accuracy, precision, and F1-scores using a dataset of 190402 images, half real and half fake that cover a wide spectrum of deep fake images. based on the evaluation metrics, each model was ranked in terms of its ability to separately describe and determine original versus generated photos with focus on detecting subtle changes eluding human senses. The comparison research shows each model's nuanced capabilities, offering light on the implications for applications in the real world like detecting and controlling the spread of deep fake information across numerous internet platforms. Our findings help towards building deep fake detection systems, further understanding the comparative performance of state of the art deep architectures. This study shall inform more effective deep fake detection systems that can guarantee trust and security in digital media environments. Accuracy achieved by model is 96%, precision score was 94%, recall value is 0.98, F1 score has a value of 0.95.

关键词： YOLO Deepfakes Accuracy image recognition Computational modeling Scalability Computer architecture Convolutional neural networks Security Long short term memory

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A Steganographic Scheme to Protect Medical Data Using Radiological images

A Steganographic Scheme to Protect Medical Data Using Radiol...

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international conference on Computational Intelligence and Communication Networks (CICN)

作者： Rr. Diajeng Alfisyahrinnisa Anandha Ntivuguruzwa Jean de La Croix Tohari Ahmad Department of Informatics Institut Teknologi Sepuluh Nopember Surabaya Indonesia African Centre of Excellence in Internet of Things University of Rwanda Kigali Rwanda

The progress in data transmission technology has underscored the significance of safeguarding information, particularly with regards to confidentiality. Researchers have therefore dedicated their attention to tackling this concern by introducing various techniques for protecting data confidentiality, including cryptography and steganography. Steganography, a data hiding method that has showcased better results in data security in images, gained significant attention from information security practitioners. Nevertheless, the existing methods show a significant tradeoff between the payload capacity and the stego image's quality, making them not applicable to sensitive domains such as military and medical. In this work, we propose a new steganography method working in medical images based on difference expansions to optimize the alleviation of the imbalance between the payload size and the imperceptibility of the stego image. Our experimental results achieve the best performance of a psnr of 75.1312 dB and the best psnr capacity of 0.6831. Our results showed an improvement in payload capacity from 0.4835 bpp by comparing the existing method to 0.6705 bpp with the same cover image labeled “Leg.”

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An Efficient and Flexible Deep Learning Method for signal Delineation via Keypoints Estimation

An Efficient and Flexible Deep Learning Method for Signal De...

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ieee international conference on signal and image Processing (ICSIP)

作者： Adrian Atienza Jakob Eyvind Bardram Peter Karl Jacobsen Sadasivan Puthusserypady Department of Health Technology Technical University of Denmark (DTU) Copenhagen Denmark Department of Cardiac Diseases Rigshospitalet Copenhagen Denmark

ISBN: (数字)9798350350920

ISBN: (纸本)9798350350937

Deep Learning (DL) methods have been used for electrocardiogram (ECG) processing in a wide variety of tasks, demonstrating good performance compared with traditional signal processing algorithms. These methods offer an efficient framework with a limited need for apriori data pre-processing and feature engineering. While several studies use this approach for ECG signal delineation, a significant gap persists between the expected and the actual outcome. Existing methods rely on a sample-to-sample classifier. However, the clinical expected outcome consists of a set of onset, offset, and peak for the different waves that compose each R-R interval. To align the actual with the expected output, it is necessary to incorporate post-processing algorithms. This counteracts two of the main advantages of DL models, since these algorithms are based on assumptions and slow down the method's performance. In this paper, we present Keypoint Estimation for Electrocardiogram Delineation (KEED), a novel DL model designed for keypoint estimation, which organically offers an output aligned with clinical expectations. By standing apart from the conventional sample-to-sample classifier, we achieve two benefits: (i) Eliminate the need for additional post-processing, and (ii) Establish a flexible framework that allows the adjustment of the threshold value considering the sensitivity-specificity tradeoff regarding the particular clinical requirements. The proposed method's performance is compared with state-of-the-art (SOTA) signal processing methods. Remarkably, KEED significantly outperforms despite being optimized with an extremely limited annotated data. In addition, KEED decreases the inference time by a factor ranging from 52x to 703x.

关键词： Deep learning image processing Estimation signal processing algorithms Electrocardiography signal processing Distance measurement

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internet of Things based Automatic System for the Traffic Violation 5

Internet of Things Based Automatic System for the Traffic Vi...

引用

5th international conference on Electrical, Electronics, Communication, Computer Technologies and Optimization Techniques (ICEECCOT)

作者： Nizzad, A. R. M. Sameer, U. M. Suhath, S. M. Nafrees, Abdul Cader Mohamed Rankothge, W. H. Kehelella, P. H. Mansoor, C. M. M. British Coll Appl Studies Sch Comp Colombo Sri Lanka Natl IT Ctr Vocat Training Author Sri Lanka Colombo Sri Lanka Sri Lanka Inst Informat Technol Fac Grad Studies Colombo Sri Lanka South Eastern Univ Sri Lanka Oluvil Sri Lanka Sri Lanka Inst Informat Technol Fac Comp Oluvil Sri Lanka South Eastern Univ Sri Lanka Fac Arts & Culture Oluvil Sri Lanka

ISBN: (纸本)9781665432726

An efficient and effective motor traffic management is crucial for any Intelligent Transportation System (ITS) to reduce traffic violations. Scientific evidence suggests that exceeding the speed limit is the most important factor that impacts the severity, fatality and other risks associated with motor vehicle collisions. However, due to the lack of proper technology used in the transport sector, there are possibilities for traffic law violators get unnoticed. Therefore, this study provides a solution based on internet of Things with image Processing technology to process the vehicle registration number to uniquely identify the vehicles that are violating traffic laws. The outcome confirms that the architecture is viable for the low-cost automation of traffic fine for the speed violation. In addition, it is recommended to use more robust mechanism to capture the real time speed of any fast-moving vehicle. In conclusion, the proposed architecture with slight modification can be deployed for many commercial test cases such as traffic management, parking, vehicle counting, data privacy, data security and more.

关键词： image Processing internet of Things Artificial Intelligence traffic violation ITS

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Relevance Propagation through Deep Conditional Random Fields

Relevance Propagation through Deep Conditional Random Fields

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international conference on Acoustics, Speech, and signal Processing (ICASSP)

作者： Xiangyu Yang Boris Joukovsky Nikos Deligiannis ETRO Department Vrije Universiteit Brussel (VUB) Brussels Belgium Imec Leuven Belgium

Conditional random fields (CRFs), a particular type of graph neural networks (GNNs), can be used to make structured predictions in machine learning, with various applications from image processing and natural language processing to recommender systems. CRFs refine the prediction of a sample by taking into account its context information. However, there is a lack of work on post-hoc explanation approaches to CRFs, especially when the model is softmax-activated like the deep mean field network (DMFN). In this paper, we bridge this gap by proposing a layer-wise relevance propagation (LRP) method based on deep Taylor decomposition to explain CRFs, especially the DMFN model. The method considers the intermediate softmax activation layers in DMFN. We use two evaluation settings: top K% deletion and insertion to evaluate the method. Experimental studies on fake news detection using the DMFN model prove the effectiveness of our explanation method compared to the other baseline methods.

关键词： signal processing algorithms Machine learning signal processing Graph neural networks Natural language processing Inference algorithms Fake news

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Blind Timing Estimation and signal Detection for RIS-Assisted Symbiotic Radio with Imperfect Symbol Synchronization

Blind Timing Estimation and Signal Detection for RIS-Assiste...

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ieee international conference on Communications (ICC)

作者： Hao Chen Ruizhe Long Ying-Chang Liang Robert Schober University of Electronic Science and Technology of China (UESTC) Chengdu P. R. China Friedrich-Alexander-University of Erlangen-Nuremberg (FAU) Erlangen Germany

ISBN: (数字)9781728190549

ISBN: (纸本)9781728190556

To support the massive internet-of- Things (IoT) network, symbiotic radio (SR) has emerged as a promising solution that enables passive IoT connections by exploiting active primary transmissions. Realizing the enhanced spectrum- and energy-efficiency promised by SR requires symbol synchronization between the primary and IoT signals, which, however, remains challenging for cost-limited IoT devices. In this paper, we investigate reconfigurable intelligent surface (RIS)-assisted SR (RSR) with imperfect symbol synchronization. Specifically, the primary transmission employs orthogonal frequency division multiplexing (OFDM), while the RIS enhances the primary transmission and concurrently transmits its secondary signal by passively backscattering the incident primary signal. Due to the unknown synchronization offset (SO) between primary and secondary signals, the reflected channel via the RIS exhibits variations within each OFDM block, consequently leading to inter-carrier interference (ICI) in the received signal. To mitigate this unfavorable effect, we propose a novel receiver design by utilizing virtual subcarriers within each OFDM block. By employing energy detection at the virtual subcarriers, the receiver can detect the arrival of the secondary signal based on the ICI. Furthermore, by compensating the loss of orthogonality in the received OFDM block, the receiver can blindly estimate the SO, thereby facilitating joint detection of primary and secondary signals. Simulation results validate that our proposed receiver significantly improves the bit error rate (BER) performance for RSR with imperfect symbol synchronization.

关键词： Symbiosis OFDM Simulation Bit error rate Symbols Receivers Reconfigurable intelligent surfaces

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Energy Bugs in Object Detection Software on Battery-Powered Devices

Energy Bugs in Object Detection Software on Battery-Powered ...

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ieee international conference on Software Reliability Engineering Workshops (ISSRE Wksp)

作者： Ippo Hiroi Fumio Machida Ermeson Andrade Department of Computer Science University of Tsukuba Tsukuba Japan Department of Computing Federal Rural University of Pernambuco Recife Brazil

ISBN: (数字)9798350367041

ISBN: (纸本)9798350367058

Object detection software is widely adopted in edge computing systems such as mobile devices, drones, and autonomous robots. These edge devices are often battery-powered and, hence, confront stringent requirements for efficient computation and energy saving. Energy bugs that cause inefficient software execution pose significant risks to object detection systems running on battery-powered edge devices. However, such risks have not been well discussed in the previous research. This paper aims to investigate potential energy bugs in object detection software running on a battery-powered device. First, we searched for reports of known energy bugs from internet forums and bug-tracking systems for YOLOv5 and Mask R-CNN (Mask Region-based Convolutional Neural Network). Although we did not find direct mentions of energy bugs, some reports hinted at energy inefficiencies. Consequently, we conducted experiments to assess the impact of energy bugs by monitoring resource utilization and battery consumption of an object detection system running YOLOv5 on a battery-powered Raspberry Pi. Our experimental results clearly show increased power consumption caused by the omission of the input image size setting and improper deployment of the unquantized model, which can be regarded as potential energy bugs in object detection software.

关键词： YOLO Potential energy Power demand image edge detection Computer bugs Software Batteries Software reliability Resource management Monitoring

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