agusgun / fakeimagedetector Goto Github PK
View Code? Open in Web Editor NEWImage Tampering Detection using ELA and CNN
License: MIT License
Image Tampering Detection using ELA and CNN
License: MIT License
Hi @agusgun Thanks for sharing your work. Would you mind helping me with some of my queries?
After going through the work, I learned that the CNN learns to predict a categorical ELA image of the input image. Am I correct?
If so, since we have already know how to convert an input image to an ELA image using the function convert_to_ela_image()
, i. e., we can calculate the 'ground truth' directly, may I know the idea of having a CNN model to do the conversion? Thanks
can u please tell what is in dataset.csv
can you share the dataset ?
Hi!
Thank you for your works. Could you provide the pre-trained model? Thanks!
hello agusgun, thank you for releasing the code, I try to download the datasets you used in the method. But there is no link to the dataset. Please tell me how can I get that dataset.
Hello,
What is the final of your program, because you don't share the results of your tests?
Thank you
I used other dataset to run your program, but it's acc was much lower than yours. Why?
hello agusgun, thank you for releasing the code, I try to download the datasets you used in the method from this link http://forensics.idealtest.org/ but there is no dataset at all...Could you please tell me how to get the datasets? Thank you very much~
Hello, I have a small doubt actually when I am trying to convert image into ELA i.e
"convert_to_ela_image('datasets/train/real/Au_ani_00001.jpg', 90)" I am getting an error that permission is denied. Can you please help where I am going wrong.
To be more specific about my error I am pasting my error below,
***** PermissionError: [Errno 13] Permission denied: 'C:\Users\saraiya.resaved.jpg' *****
Any help will be highly appreciated thanks in advance.
Taking the project to an organized directory structure can help developers use this model and write relevant APIs. Kudos to the amazing project. Accuracy is not bad too
untuk dataset .csv parameter yang diambil apa saja ?
I downloaded this dataset https://www.kaggle.com/sophatvathana/casia-dataset
but it has no any description, and some strange folder tree
├───CASIA1
│ ├───Au
│ ├───ela
│ └───Sp
├───CASIA2
│ ├───Au
│ └───Tp
└───__MACOSX
==├───CASIA1
==│ ├───Au
==│ └───Sp
==└───CASIA2
====├───Au
====└───Tp
which one should i use for train, which for test? which one is original pic which one is modified?
also i know the csv format
file_name,1 or 0 (fake or real image)
example for real image:
'datasets/train/real/Au_ani_00001.jpg',0
but i have no idea which folder should i use for source...
I appreciate for your great work, and I want repeat it by myself :- )
=========================================
so. i made this
I upload zip to google drive
unzip it to '/content/gdrive/My Drive/casia_dataset/
in google colab i generated csv by following code
is it right?
import os
path_orig = '/content/gdrive/My Drive/casia_dataset/CASIA2/Au/'
path_modif = '/content/gdrive/My Drive/casia_dataset/CASIA2/Tp/'
folder_orig = os.listdir()
folder_modif = os.listdir()
strings = []
for file in os.listdir(path_orig):
try:
if file.endswith('jpg'):
if int(os.stat(path_orig + file).st_size) > 10000:
line = path_orig + file + ',1\n'
strings.append(line)
except:
print(path_orig+file)
for file in os.listdir(path_modif):
try:
if file.endswith('jpg'):
if int(os.stat(path_modif + file).st_size) > 10000:
line = path_modif + file + ',0\n'
strings.append(line)
except:
print(path_modif+file)
for line in strings:
with open('/content/gdrive/My Drive/casia_dataset/dataset.csv', 'a') as f:
f.write(line)
A declarative, efficient, and flexible JavaScript library for building user interfaces.
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
An Open Source Machine Learning Framework for Everyone
The Web framework for perfectionists with deadlines.
A PHP framework for web artisans
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
Some thing interesting about web. New door for the world.
A server is a program made to process requests and deliver data to clients.
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
Some thing interesting about visualization, use data art
Some thing interesting about game, make everyone happy.
We are working to build community through open source technology. NB: members must have two-factor auth.
Open source projects and samples from Microsoft.
Google ❤️ Open Source for everyone.
Alibaba Open Source for everyone
Data-Driven Documents codes.
China tencent open source team.