Fake Currency Detection using Deep Learning - JETIR Fake Currency Detection using Deep Learning Dr Madhuri Borawake1, Umesh ManePati2, Abhishek Karale3, Cinthiya Salve4, Prasad Suryawanshi5 Computer Engineering Department, PDEA’S College Of Engineering Manjari(BK), Pune-412307, India 1madhuri borawake@gmail com 2umeshmane2668@gmail com 3agkarale01@gmail com 4salvecinthia93@gmail com
Counterfeit Bill Detection Algorithm using Deep Learning Counterfeit Bill Detection Algorithm using Deep Learning Soo-Hyeon Lee1 and Hae-Yeoun Lee2,* 1Undergraduate Student, 2Professor 1,2 Department of Computer Software Engineering, Kumoh National Institute of Technology, 61 Daehak-ro, Gumi, Gyeongbuk, 39177, South Korea *Corresponding author ORCIDs: 10000-0002-3372-5660, 20000-0002-6081-1492
deepfake-detection · GitHub Topics · GitHub This projects aims in detection of video deepfakes using deep learning techniques like RestNext and LSTM We have achived deepfake detection by using transfer learning where the pretrained RestNext CNN is used to obtain a feature vector, further the LSTM layer is trained using the features For more details follow the documentaion
(PDF) Deep Learning for Counterfeit Bill Detection - Academia. edu Nearly 100% detection accuracy has been achieved Keywords-counterfeit bill detection; forgery device detection; deep learning; convolutional neural network Our system is designed to eliminate all of the above problems through the use of deep learning techniques by detecting the features of currencies and determining whether its fake with
Deepfake Detection with Deep Learning: Convolutional Neural Networks . . . trained models achieved detection accuracies of 99 26%, 95 73% and 81% for the FF++ raw, FF++ HQ and FF++ LQ test data, respectively In [26], the authors utilized the VGG-Face [36] with ResNet50 architecture for its detection model creation They monitored the neuron behaviors of deep face recognition to detect the fake faces
Deep fake Detection using deep learning techniques: A Literature Review . . . Deep learning is a sophisticated and adaptable technique that has found widespread use in fields such as natural language processing, machine learning, and computer vision It is one of the most recent deep learning-powered applications to emerge Deep fakes are altered, high-quality, realistic videos images that have lately gained popularity Many incredible uses of this technology are being
A systematic literature review on deepfake detection techniques - Springer Big data analytics, computer vision, and human-level governance are key areas where deep learning has been impactful However, its advancements have also led to concerns over privacy, democracy, and national security, particularly with the advent of deepfake technology Deepfakes, a term coined in 2017, primarily involve face-swapping in videos Initially easy to detect, rapid advancements in
CURRENCY FEATURE EXTRACTION USING IMAGE PROCESSING TECHNIQUES A Project which is related to camera resolution An IJAER paper "Counterfeit Bill Detection Algo-rithm using Deep Learning" [3] proposed an algorithm that can detect fake bills using deep leaning The researchers used a convolutional neural network (CNN) to increase the speed and performance of fake bill detection
aprameya2001 Fake-Currency-Detection-System - GitHub Fake Notes: Contains images of fake 500 and 2000 rupee notes You can use these images to test the system if you want 500_testing ipynb: This notebook processes input image of 500 denomination currency bills 2000_testing ipynb: This notebook processes input image of 2000 denomination currency bills
Fake Currency Detection Using Deep Learning Algorithm - IJARCCE this problem now the trend is towards deep learning, since it is a multilayer neural network The deep neural network is effective for different application in real time II LITERATURE SURVEY Gouri Sanjay Tele et al [5] proposed the detection of Fake Indian currency Security highlights of currency are basic for deciding genuine and fake money
Real Time Fake Currency Note Detection using Deep Learning - Academia. edu To show the performance of the algorithm, experiments are performed using original bills and counterfeit bills forged with different manufacturers #39; printers Nearly 100% detection accuracy has been achieved Keywords-counterfeit bill detection; forgery device detection; deep learning; convolutional neural network
Deepfake Detection: A Systematic Literature Review Over the last few decades, rapid progress in AI, machine learning, and deep learning has resulted in new techniques and various tools for manipulating multimedia Though the technology has been mostly used in legitimate applications such as for entertainment and education, etc , malicious users have also exploited them for unlawful or nefarious purposes For example, high-quality and realistic
DEEPFAKE DETECTION TECHNIQUES: A REVIEW - viva-technology. org Remarkable improvements in the field of Deep Learning have led to the growth of Deepfake videos With the help of Deep Learning architectures such as Generative Adversarial Neural Networks (GANs) and autoencoders and a considerable amount of footage of a target subject, anyone can create such convincing fake videos [4]
Deepfake detection using Deep Learning (ResNext and LSTM) This projects aims in detection of video deepfakes using deep learning techniques like RestNext and LSTM We have achived deepfake detection by using transfer learning where the pretrained RestNext CNN is used to obtain a feature vector, further the LSTM layer is trained using the features For more details follow the documentaion Topics
A Deep Learning App for Counterfeit Banknote Detection in the WAEMU To show the performance of the algorithm, experiments are performed using original bills and counterfeit bills forged with different manufacturers #39; printers Nearly 100% detection accuracy has been achieved Keywords-counterfeit bill detection; forgery device detection; deep learning; convolutional neural network