Fake image detection project

Fake (Photoshopped) Image Detection using Machine Learnin

  1. Fake image detection projects introduces two different levels of analysis for the image. At first level, it checks the image metadata. Image metadata is not that much reliable since it can be altered using simple programs
  2. For this project, we will be using only the train set. It contains 2 directories — one containing fake images and their corresponding masks and the other containing pristine images. Mask of a fake image is a black and white (not grayscale) image describing the spliced area of the fake image
  3. Classifies a given aadhaar image to real or fake by doing two levels of analysis. python metadata opencv fake-images image-processing fake signatures python27 lbph opencv-python ela opencv2 error-level-analysis fake-image-detection. Updated on Aug 20, 2020. Python
  4. The sensitivity of the hsv characteristics of an image means the difference ratio must be very high to be sure the image is fake. In this case the difference in the Hue is 92.02%, the difference in the Saturation is 96.69% and the difference in the Value is 95.38%. 3. Counting Green Strips
  5. Project objective. Combine the implementation of error-level analysis (ELA) and deep learning to detect whether an image has undergone fabrication or/and editing process or not, e.g. splicing
  6. Detection of Fake currency using Image Processing P 1 PM.Deborah. P 2 PC.Soniya Prathap.M.E Infant Jesus college of engineering and technology.. Abstract: The main objective of this project is fake currency detection using the image processing. Fake currency detection is a process of finding the forgery currency. After choose the image apply pre

Image forgery detection

  1. g, Conversion And Watermarking. 5 Free Photoshop Alternatives - Best Photo & Image Editor Onlin
  2. The conferred approach offers AN economical technique of pretend currency detection supported physical look. 3 necessary security measures explored for faux currency detection are the protection thread, run brand, and identification mark. Image process algorithms are applied to extract the options. To mix the multiple options, call a score of.
  3. The Pytorch implemention of Deepfake Detection based on Faceforensics++ - HongguLiu/Deepfake-Detection and you can use the mesonet network in this project. Install & Requirements. You can train the model with full images, but we suggest you take only face region as input. Pretrained Model
  4. Detection of such fake images is inevitable for the unveiling of the image based cybercrimes. Forging images and identifying such images are promising research areas in this digital era. The complete project is available in GitHub [6]. 4000 real and fake images for training. Remaining images were used for testing of the neural network

sir my project on facial expression recognition in humans using image processing sir my mail id smitadhon11@gmail.com sir i done preprocessing code, features extractions on face image code, centroides of each features, my using distance vector method is calculate distance vector these code i done and correct output but next steps i face problem plz send me matlab code for facial expression. A picture is worth a thousand words.In the modern world, anyone can alter and edit images using a wide variety of tools. So, it is important to make sure t.. Creating fake images and videos such as Deepfake has become much easier these days due to the advancement in Generative Adversarial Networks (GANs). Moreover, recent research such as the few-shot learning can create highly realistic personalized fake images with only a few images. Therefore, the threat of Deepfake to be used for a variety of malicious intents such as propagating fake images. Fake News Detection in Python. In this project, we have used various natural language processing techniques and machine learning algorithms to classify fake news articles using sci-kit libraries from python. Getting Started. These instructions will get you a copy of the project up and running on your local machine for development and testing.

fake-image-detection · GitHub Topics · GitHu

DeepFakes and Beyond: A Survey of Face Manipulation and Fake Detection. deepfakes/faceswap • 1 Jan 2020 The free access to large-scale public databases, together with the fast progress of deep learning techniques, in particular Generative Adversarial Networks, have led to the generation of very realistic fake content with its corresponding implications towards society in this era of fake news In this project we have made fake currency note CAMERA detection technique using MATLAB and other applications of image processing. In the project setup, note is placed in front of camera to check whether it is fake or genuine. The camera pictures of notes are analyzed by MATLAB program installed II Advances in photo editing and manipulation tools have made it significantly easier to create fake imagery. Learning to detect such manipulations, however, remains a challenging problem due to the lack of sufficient amounts of manipulated training data. In this paper, we propose a learning algorithm for detecting visual image manipulations that. When it comes to detection of fake images and fact-checking based on image analysis, deep learning techniques, and CNNs specifically, have been proven very successful, since they allow face recognition and classification (Bouchra et al., 2019), image segmentation, object detection and characterisation (Dhillon, Verma, 2020, Rajagopal, Joshi. Contact Best Phd Projects Visit us: http://www.phdprojects.org


Limitations, improvements, and further work. The primary restriction of our liveness detector is really our limited dataset — there are only a total of 311 images (161 belonging to the real class and 150 to the fake class, respectively).. One of the first extensions to this work would be to simply gather additional training data, and more specifically, images/frames that are not. authors release fake news. As such, the goal of this project was to create a tool for detecting the language patterns that characterize fake and real news through the use of machine learning and natural language processing techniques. The results of this project demonstrate the ability for machine learning to be useful in this task Figure 1 is the flowchart that shows the general methods used to detect fake currency using image processing A. Image Acquisition The image of the currency that has to be checked or verified as a genuine currency is taken as an input for the system. The input image can be acquired using techniques like scanning the image or clicking a picture.

Path Finder was sent to Mars in 1998. This was achievement a great which detected the secrets of Mars. This project deals with RF controlled robot and it is a prototype for the Path Finder Currency Counting Machine with Fake Note Detection The currency counting machine or CCM is one of the miracle of the science Image Forgery and fake image circulation have become very widespread with availability of various tools to manipulate images and medium to spread the images. There are many types of manipulation done on images. In this project we have tried to detect few of them with good accuracy Note: For the images shown above, we display 3 key pieces of information for each picture. The first piece is the probability of each being real and fake, respectively. The second represents the model prediction (0 for real, 1 for fake), while the third info represents the actual label •Do not clearly detect the fake currency •Low Psnr Value . Proposed work: The system will work on two images, one is original image of the paper currency and other is the test image on which verification is to be performed. The proposed algorithm for the discussed paper currency verification system is presented as follow Image recognition is the ability of AI to detect the object, classify, and recognize it. The last step is close to the human level of image processing. The best example of picture recognition solutions is the face recognition - say, to unblock your smartphone you have to let it scan your face

How to Spot Fake Images Verifying and certifying photographs as authentic is big business in the photography industry, especially for photojournalists, media editors, competition judges, police services, and courts. Online and custom services analyze an image's metadata, using complex and evolved algorithms to detect changes In this project we have made fake currency note detection technique using MATLAB and feature extraction with HSV color space and other applications of image processing. In the project setup, note is placed in front of camera to check whether it is fake or genuine. The camera pictures of notes are analyzed by MATLAB program installed on computer.

ABSTRACT. This Project comes up with the applications of NLP (Natural Language Processing) techniques for detecting the 'fake news', that is, misleading news stories that comes from the non-reputable sources. Only by building a model based on a count vectorizer (using word tallies) or a (Term Frequency Inverse Document Frequency) tfidf matrix. The image below, taken from the official website, clearly illustrates the task. A news headline and article are taken and the relation between them is classified into 4 classes - (unrelated, agrees, disagrees, discusses) The neural network architecture we built is shown below. Fake-News-Detection is maintained by Anirudh42 List of Simple Image Processing Projects for ECE and CSE Students. This article also Contains Image Processing Mini Projects (which includes Digital Image Processing Projects, Medical Image Processing Projects and so on) for Final Year Engineering Students with Free PDF Downloads, Project Titles, Ideas & Topics with Abstracts & Source Code Downloads Advanced Projects, Big-data Projects, Cloud Based Projects, Django Projects, Machine Learning Projects, Python Projects on Fake Product Review Detection and Sentiment Analysis Now days, online buyer are so much aware and sensitive to product reviews

GitHub - agusgun/FakeImageDetector: Image Tampering

this project face detection system with face recognition is Image processing. The software requirements for this project is matlab software. Keywords: face detection, Eigen face, PCA, matlab Extension: There are vast number of applications from this face detection project, this project Fake News Detection using Machine Learning NLP. ₹ 1,501.00. instamojo payment gateway only for indian. Problem Facing On Download Please Contact Here. Other country Contact Here : projectworldsofficial@gmail.com. whatsapp - +916263056779. Fake News Detection using Machine Learning NLP quantity. Add to cart Deepfake Detection with Python. There have been many reports of fake videos of popular celebrities or politicians. These fake videos are difficult to detect with the naked eye and are becoming a major problem in society. Also, Read - Machine Learning Full Course for free

When it comes to detection of fake images and fact-checking based on image analysis, deep learning techniques, and CNNs specifically, have been proven very successful, since they allow face recognition and classification (Bouchra et al., 2019), image segmentation, object detection and characterisation (Dhillon, Verma, 2020, Rajagopal, Joshi. The main working processes of Currency Sorting Machine are image acquisition and recognitions. It is a technique named optical, mechanical and electronic integration, integrated with calculation, pattern recognition (high speed image processing), currency anti-fake technology, and lots of multidisciplinary techniques In cases where the open -source project did not have any available fake images, the research team ran their released code to generate 1,000 synthetic images. Given that testing images may come from an unseen generative model in the real world, the research team mimicked real-world applications by performing cross-validation to train and. All work was completed for HackMIT in under 24 hours. We also won awards for 'best use of data' and 'best use of machine learning for the common good.'Read. However, The main objective of this project is fake currency detection using MatLab. This process can be automated in a computer using the application software. The basic logic is developed using Image acquisition, gray scale conversion ,edge detection, image segmentation, feature extraction and comparison

4 Free Fake Image Detector - Analyze Photoshopped Photo

The dataset I will use in this task for fake currency detection can be downloaded from here. The dataset contains these four input characteristics: The variance of the image transformed into wavelets. The asymmetry of the image transformed into wavelets. Kurtosis of the image transformed into wavelets. Image entropy fake news detection methods. Fake news detection on social media is still in the early age of development, and there are still many challeng-ing issues that need further investigations. It is neces-sary to discuss potential research directions that can improve fake news detection and mitigation capabili-ties Colour detection is necessary to recognize objects, it is also used as a tool in various image editing and drawing apps. This is the 10th project in the DataFlair's series of 20 Python projects. I suggest you to bookmark the previous projects

Fake Currency detection using Image Processin

Research Problem The project is concerned with identifying a solution that could be used to detect and filter out sites containing fake news for purposes of helping users to avoid being lured by clickbaits. It is imperative that such solutions are identified as they will prove to be useful to both readers and tech companies involved in the. Image Segmentation Edge Detection Feature Extraction Comparison Output Fig -2: Flow Chart of Digital Image Processing Method to Detect Fake Note In image smoothening, while using camera or scanner and perform image transfer, some noise will appear on the image. The important step of removing noise is done by image smoothening Deepfake Video Detection Using Recurrent Neural Networks David Guera Edward J. Delp¨ Video and Image Processing Laboratory (VIPER), Purdue University Abstract In recent months a machine learning based free software fake celebrity pornographic videos or revenge porn [5] Fake News DetectionEdit. Fake News Detection. 72 papers with code • 5 benchmarks • 19 datasets. Fake news detection is the task of detecting forms of news consisting of deliberate disinformation or hoaxes spread via traditional news media (print and broadcast) or online social media (Source: Adapted from Wikipedia) 3| Real and Fake Face Detection. This dataset contains expert-generated high-quality photoshopped face images where the images are composite of different faces, separated by eyes, nose, mouth, or whole face. Size: The size of the dataset is 215MB. Projects: This dataset can be used to discriminate real and fake images

Keywords: Stance Detection, Natural Language Processing (NLP), Random Forest. 1. INTRODUCTION Fake news has been around for decades and is not a new concept. However, the dawn of the social media age which can be approximated by the start of the 20th century has aggravated the generation and circulation of fake news many folds. Fake In this data science project idea, we will use Python to build a model that can accurately detect whether a piece of news is real or fake. We'll build a TfidfVectorizer and use a PassiveAggressiveClassifier to classify news into Real and Fake. We'll be using a dataset of shape 7796×4 and execute everything in Jupyter Lab Relevance: In this proposed system, our relevance is to focus on detection of fake currencies which is spreaded in Indian market also our main goal is to use image processing technique and recognize original currency. Relevance of our project is similar to currency recognition system using neural networks

GitHub - HongguLiu/Deepfake-Detection: The Pytorch

The fake currency detection using image processing was implemented on MATLAB. Features of currency note like serial number, security thread, Identification mark, Mahatma Gandhi portrait were extracted. The process starts from image acquisition to calculation of intensity of each extracted feature. The syste We are going to build this project in two parts. In the first part, we will write a python script using Keras to train face mask detector model. In the second part, we test the results in a real-time webcam using OpenCV. Make a python file train.py to write the code for training the neural network on our dataset. Follow the steps based detection approaches to detect deepfakes. How-ever, binary-classification based methods generally require a large amount of both real and fake face images for train-ing, and it is challenging to collect sufficient fake images data in advance. Besides, when new deepfakes genera-tion methods are introduced, little deepfakes data will b Fake News Challenge Stage 1 (FNC-I): Stance Detection. Fake news, defined by the New York Times as a made-up story with an intention to deceive 1 , often for a secondary gain, is arguably one of the most serious challenges facing the news industry today. In a December Pew Research poll, 64% of US adults said that made-up news has.

(PDF) Fake Image Detection Using Machine Learning Yogesh

Matlab Projects, Image Quality Assessment for Fake Biometric Detection Application to Iris, Fingerprint, and Face Recognition, biometric authentication, biometric recognition frameworks, 2D face, Matlab Source Code, Matlab Assignment, Matlab Home Work, Matlab Hel Today, we are introducing our fourth python project that is gender and age detection with OpenCV. It is very interesting and one of my favorite project. DataFlair has published more interesting python projects on the following topics with source code: Fake News Detection Python Project. Parkinson's Disease Detection Python Project Steps Involved to implement Smile Detection and Selfie Capture Project. We first import the openCV library. Now start webcam in the second line using the VideoCapture function of cv2. Then, include haarcascade files in the python file. Video is nothing but a series of images so we will run an infinite while loop for the same

Extensive experiments are conducted on the fake image dataset generated by the advanced GAN technique. Experimental results demonstrate the proposed scheme outperforms state-of-the-art methods and achieves the promising average detection accuracy (above 99%) under several post-processing attacks, such as Gaussian blurring and so on First, even the best forensics detector will have some trade-off between true detection and false-positive rates. Since a malicious user is typically looking to create a single fake image (rather than a distribution of fakes), they could simply hand-pick the fake image which happens to pass the detection threshold These detection techniques feed into a master model that tells users how likely it is that an image has been manipulated. Fake images are among the harder things to verify, especially with the.

Our dataset contains expert-generated high-quality photoshopped face images. The images are composite of different faces, separated by eyes, nose, mouth, or whole face. You may wonder why we need these expensive images other than images automatically generated by computers. Say we want to train a classifier for real and fake face images Due to the significant advancements in image processing and machine learning algorithms, it is much easier to create, edit, and produce high quality images. However, attackers can maliciously use these tools to create legitimate looking but fake images to harm others, bypass image detection algorithms, or fool image recognition classifiers

Deepfake detection is a more constrained problem than general object detection, but these types of fine-grained visual classification seem to provide an edge when figuring out exactly which parts of a face to drop. Architectures. All winners used pretrained EfficientNet networks, which were fine-tuned only on the DFDC training data Unique images: 4,863,645 Banned users: 8,697 Statistics last updated 7 minutes ag features are indeed useful and (2) fake review detection in the real-life setting is considerably harderthan in the AMT data setting in [36] which yielded about 90% accuracy. Note that a balanced data (50% fake and 50% non-fake reviews) was used as in [36]. Thus, by chance, the accuracy should be 50%. Results in the natura In this fake news detection project, we are using Supervised learning. Check out more here. Image compression via k-means Clustering. Varun Garg in Web Mining [IS688, Spring 2021

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Subscribe to our channel to get this project directly on your emailDownload this full project with Source Code from https://enggprojectworld.blogspot.comhttp.. Fake News Detection Julio C. S. Reis, Andre Correia, Fabrıcio Murai, Adriano Veloso, and Fabrıcio Benevenuto Universidade Federal de Minas Gerais Editor: Erik Cambria, Nanyang Technological University, Singapore Abstract—A large body of recent works has focused on understanding and detecting fake Source-to-Target: where we reenact over 1000 videos with new facial expressions extracted from other videos, which e.g. can be used to train a classifier to detect fake images or videos. Selfreenactment: where we use Face2Face to reenact the facial expressions of videos with their own facial expressions as input to get pairs of videos, which e. Many algorithms and methods, most of which use the huge volume of unstructured data generated from social networks, have been proposed for the detection of fake profiles. This study presents a survey of the existing and latest technical work on fake profile detection A composite of current Computer Vision and Medical Imaging Projects (Image by Author) (AI) and computer science that enables automated systems to see, i.e. to process images and video in a human-like manner to detect and identify objects or regions of importance, predict an outcome or even alter the image to a desired format [1]. Most popular use cases in the CV domain include automated.

The 3 Phases. To create a complete project on Face Recognition, we must work on 3 very distinct phases: Face Detection and Data Gathering. Train the Recognizer. Face Recognition. The below block diagram resumes those phases: 2. Installing OpenCV 3 Package Proposed system is based on image processing and makes the process automatic and robust. Shape information are used in our algorithm. Original Note Detection Systems are present in banks but are very costly. We are developing an image processing algorithm which will extract the currency features and compare it with features of original note image Facebook launches $10m deepfake detection project. 09 Sep 2019 3 Facebook, Fake One of the networks focuses on producing a lifelike image. The other network checks the first network's output. people about who the person is in the image. In our project, we have studied and implemented a pretty simple but very effective face detection algorithm which takes human skin colour into account. Our aim, which we believe we have reached, was to develop a method of face recognitio

Top 100+ Image Processing Projects - Source Code and

Some examples of previous developed solutions include algorithms in open-set recognition, meta-recognition, multimedia phylogeny, spoofing detection, forgery detection in images. His group recently partnered-up with Polimi and others worldwide in the DARPA Medifor project in an effort to detect multimedia forgeries and hint at media provenance. Rapid pace of generative models has brought about new threats to visual forensics such as malicious personation and digital copyright infringement, which promotes works on fake image attribution. Existing works on fake image attribution mainly rely on a direct classification framework. Without additional supervision, the extracted features could include many content-relevant components and. About this project. This is a simple example of running face detection and recognition with OpenCV from a camera. NOTE: I MADE THIS PROJECT FOR SENSOR CONTEST AND I USED CAMERA AS A SENSOR TO TRACK AND RECOGNITION FACES.So, Our GoalIn this session, 1. Install Anaconda 2. Download Open CV Package 3. Set Environmental Variables 4. Test to confirm 5 Lane Detection. In order to improve accuracy and robustness of the lane detection in complex conditions, such as the shadows and illumination changing, a novel detection algorithm was proposed based on machine learning. After pretreatment, a set of haar-like filters were used to calculate the eigenvalue in the gray image f(x,y) and edge e(x,y)

Identify Photoshopped (Fake) Images - Neural Network

Fake currency detection. Fake currency is impersonation currency created without the lawful authorize of the state or government. Delivering or utilizing fake currency is a type of misrepresentation or fraud. In the course of recent years, because of the immense innovative advances in shading printing, copying and examining, falsifying issues. INTRODUCTION. In February 2019 WITNESS in association with George Washington University brought together a group of leading researchers in media forensics and detection of deepfakes and other media manipulation with leading experts in social newsgathering, UGC and opens-source intelligence (OSINT) verification and fact-checking currency detection using image processing and computer vision techniques in the past. Various neural network based system have been used to detect the fake paper currency. Megha Thakur et al. [1] employed Digital Image Processing method and MATLAB technique to detect the fake currency notes. Naina Shende et al. [2] employed the OCR (Optica I am working on a fake currency detection project and i want to get the security thread or the silver bromide thread.how can i be able to get the security thread as i am new to matlab and image processing? please help and few suggestion on the project. i am basically working on the indian currency.please help me out Facebook suggests that deepfake detection may also be improved by using techniques that go beyond the analysis of an image or video itself, such as assessing its context or provenance


Albeit stance detection approaches have been proposed in the literature , , , , not many rumour or fake news detection systems, which employ such stance as feature, exist. Jin et al. [50] , following [92] , has exploited topic models [7] to identify conflicting viewpoints in microblogs, and has built a credibility network to determine the. Therefore, CNN is employed to model textual latent features for fake news detection. Image and text analysis of news using CNN . 2. Image Branch: Similar to the text branch, we use two types of features: Visual Explicit Features XI e and Visual Latent Features XI l. In order to obtain the visual explicit features, we firstly extract the.

FDFtNet: Facing Off Fake Images using Fake Detection Fine

Namely, it can't detect fake images created by a generative model that it hasn't been trained on. And there are countless such models in use. The goal of the project, as presented by the. So fake currency detection is a difficult task by simple visual inspection and use of digital image processing algorithms come to play a vital role. The conceivable arrangements are there, to utilize either chemical properties of the currency or to utilize its physical appearance for detection

The world is becoming increasingly anxious about the spread of fake videos and pictures, and Adobe — a name synonymous with edited imagery — says it shares those concerns. It's released new. Abstract. People can get infected with fake news very quickly with misleading words and images and post them without any fact-checking. The social media life has been used to distribute counterfeit data, which has a significant negative influence on individual consumers and on a wider community MATLAB projects for engineering students are broadly employed in signal processing, image, research, academic and industrial enterprises. This was first implemented by researchers and engineers in control engineering. Further, it is rapidly spread into many other domains. At present, these projects are applicable in different fields like education for teaching subjects like numerical analysis. Pre-process Images: We ran facial-detection software to extract out the face in each im-age. We then re-scaled the croppings, and manually eliminated poor images. As a pre-processing step for the CNN, we also applied a Gaussian filter to the images, and subtracted the mean-image of the training set from each image. In order to get more out of. #To save the trained model model.save('mask_recog_ver2.h5') How to do Real-time Mask detection . Before moving to the next part, make sure to download the above model from this link and place it in the same folder as the python script you are going to write the below code in. . Now that our model is trained, we can modify the code in the first section so that it can detect faces and also tell.

GitHub - nishitpatel01/Fake_News_Detection: Fake News

The complete guide on how to combine Python, Machine Learning and NLP to successfully detect fake news. Filippos Dounis. Apr 2, 2020 · 6 min read. Photo by Filip Mishevski on Unsplash Second, exploiting this auxiliary information is nontrivial in and of itself as users' social engagements with fake news produce data that is big, incomplete, unstructured, and noisy. This quick guide is based on a recent survey [1] that presents issues of fake news detection on social media, state-of-the-art research findings, datasets, and. Improvements in malware detection techniques have grown significantly over the past decade. These improvements have resulted in better security for systems from various forms of malware attacks. However, it is also the reason for continuous evolution of malware which makes it harder for current security mechanisms to detect them. Hence, there is a need to understand different malwares and. Load a sample image of the speaker to identify him in the video: image = face_recognition.load_image_file(sample_image.jpeg) face_encoding = face_recognition.face_encodings(image)[0] known_faces = [ face_encoding, ] All this completed, now we run a loop that will do the following: Extract a frame from the vide

Liveness detection algorithms analyse images or videos and decide whether they come from a live person or a fake. Methods used are motion and/or texture analysis as well as artificial intelligence (AI). To cope with various presentation attacks, the most promising liveness detection combines these technologies The edge detection and image segmentation were used to make a comparison between the original and the counterfeit notes. Snehlata et al. presented a UML activity model designed to represent the dynamic aspects for identification of fake currency for Rs 2000 currency note for Indian rupee In my project, my main motivation is to find out whether a news article it is fake or not. At the end, the user will know the probability of the news being fake or not

In this digital age ‍♂️ where hoax news is present everywhere in digital platforms, there is an ultimate need for fake news detection, and this model serves its purpose by being the need of the hour tool. In future, Multi-Modal for fake news detection can be made which can detect fake news based on both images and caption Face detection is a computer vision problem that involves finding faces in photos. It is a trivial problem for humans to solve and has been solved reasonably well by classical feature-based techniques, such as the cascade classifier. More recently deep learning methods have achieved state-of-the-art results on standard benchmark face detection datasets We find that best current discriminators can classify neural fake news from real, human-written, news with 73% accuracy, assuming access to a moderate level of training data. Counterintuitively, the best defense against Grover turns out to be Grover itself, with 92% accuracy, demonstrating the importance of public release of strong generators Face detection is the process of identifying one or more human faces in images or videos. It plays an important part in many biometric, security and surveillance systems, as well as image and video indexing systems. This face detection using MATLAB program can be used to detect a face, eyes and upper body on pressing the corresponding buttons.

(PDF) Opinion Spam Detection in Online Reviews

image: The location of the input image for text detection & recognition. east: The location of the file having the pre-trained EAST detector model. min-confidence: Min probability score for the confidence of the geometry shape predicted at the location. width: Image width should be multiple of 32 for the EAST model to work well On an episode of Radiolab recorded earlier this year, host Simon Adler leads us down a fascinating and somewhat terrifying path into the future of fake news, where videos of real people—like a U.S. president—can be made to say fake things. While we have strategies for identifying fake images, a new wave of audio and video manipulation tools have the potential to twist reality even further I was experimenting with a deep Convolutional Neural Network to perform this task, i.e. differentiating between real and fake faces with 2D image from monocular camera. The CNN is trained with about 10k real images and 10k fake images with conside..

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