ILLUMINATING DEEPFAKES
- An Anti-Deepfake TechnologyBy Team RnD 4
Dr. D.Y.PATIL SCHOOL OF ENGINEERING
PUNE
DEPARTMENT OF COMPUTER ENGINEERING
Presented by: R&D Group 4
Team Members
1. Mrityunjay Mishra PRN No.71920213c
2.Shaikh Azim
PRN No.71920289C
3.Allaudin Shaikh
PRN No.71920287G
4.AnandKumar Rai PRN No.71845962J
UNDER THE GUIDANCE OF
Prof. Sunil Rathod
Area of Project: A. I & Digital Forensics
CONTENTS
Track 1
PROBLEM STATEMEMT
MOTIVATION
OBJECTIVES
INTRODUCTION
LITERATURE REVIEW
PROJECT FEASIBILITY & Scope
REQUIREMENTS
ARCHITECTURE
CONTENTS
Track 2
ALGORITHMS
STRUCTURAL DIAGRAMS
BEHAVIORAL DIAGRAMS
ADVANTAGES
LIMITATIONS
FUTURE WORK
CONCLUSION
REFRENCE
PROBLEM STATEMENT
120
100
100
90
80
80
70
60
50
40
20
0
Percentage
Due to an advent rise in technology, it has almost become very difficult to
distinguish between deepfakes and real video. Deepfake videos are usually
made by using StyleGAN’s(Generative Adversial Network) framework
and other deep learning technologies. The danger of this is that technology
can be used to make people believe something is real when it is not
actually. For finding a secured solution for this we need to develop an antideepfake technology that will be implementing deepfake detection models
with different features using a deep learning approach to detect these
videos by isolating, analyzing, and verifying the content.
MOTIVATION
120
100
100
90
80
80
70
60
50
40
20
0
Percentage
Few applications such as FaceApp and Fake App are built on this
technology to be used on mobile and desktop, which results in affecting a
person’s integrity. Identifying, authenticating, and categorizing these
videos has become a necessity. It’s not just about these latest advancements
in creating fake images and video, it is the injection of these techniques
into an ecosystem that is already promoting fake news, sensational, and
conspiracy theories.
OBJECTIVE
90%
CNN WILL BE USED TO
CLASSIFY REAL OR FAKE
AMONGST ALL.
DEDIDACETD FUNCTIONS
IMAGE EXTRACTIONVIDEOS WILL BE
CONVERTED INTO FRAMES
EXPLOITING FRAME-LEVEL
SCENE INCONSISTENCY
10%
20%
45%
75% EXPLOITING MULTIPLE
ANOMALIES & LEADS TO A
FLICKERING PHENOMENON
50%
COMPARING EXCLUSIVE
BLUR INCONSISTENCY BY
THE SYNTHESIZED BLURRED
AREAS
INTRODUCTION TO DOMAIN
ARTIFICIAL
INTELLIGENCE
.
AI which stands for artificial
intelligence refers to systems or
machines that mimic human
intelligence to perform tasks
and can iteratively improve
themselves based on the
information they collect.AI isn’t
intended to replace humans. It’s
intended to significantly
enhance human capabilities and
contributions. That makes it a
very valuable business asset.
DIGITAL FORENSICS
Digital forensics is a
branch of forensic
science encompassing
the recovery,
investigation,
examination and
analysis of material
found in digital
devices, often in
relation to mobile
devices and computer
crime.
Machine learning is a branch of artificial intelligence (AI) and computer science which focuses on the use of data and
algorithms to imitate the way that humans learn, gradually improving its accuracy
LITERATURE REVIEW
PAPERS
Deepfake Video Detection Using RNN
AUTHOR
David Guera
METHODS
Video and image processing
Laboratory
Exposing Deepfake videos By Detecting
Warping Artifacts
Yuezun Li
Eyes blinking, Head poses using Warping
Artifacts
The Deepfake Detection Challenge Dataset
Brian Dolhansky
Audio Visual affective cues
Deepfake Video Detection Through Optical Irene Amerint
Flow Based CNN
CNN, Generated imagess
UPDATION
ADDITION
SCOPE
Adding additional images to
the dataset, as having more
data will inherently make the
models more accurate.
Update the associated WebApp to be more user-friendly
and have more features for
deepfake image classification.
EXPANSION
FACEFORENSICS
Expand the scope of this
project to include being able to
scan video for deepfakes as
this is increasingly becoming
problematic technology.
It consists of an encoder and a
decoder, where at the encoder
side, the image is given as input,
and scaling is done using a
convolutional layer
REQUIREMENTS
Processor :
2.0Gz
HARD
DISK: 128
GB
RAM : 8 GB
OS:
WINDOWS
7 OR
ABOVE
LANGUAG
E:
PYTHON 3,
html
High end
integrated
tools and
extensions
SYSTEM ARCHITECTURE
STRUCTURAL ARCHITECTURE
CLASS DIAGRAM
BEHAVIORAL ARCHITECTURE
DOWNLOAD THE
DATASET
Step #1
OPTIMIZATION AND
TRAINING WITH CNN
FACE DETECTION
Step #2
ENCODING LABELS
Step #3
Step #4
READING GREYSCALES &
SPLITTING DATA PROCESSES
Step #5
ADVANTAGES & LIMITATIONS
ADVANTAGES:
• Machine Learning is one of the accurate techniques that can be applied
in the system for detecting deepfakes.
• We all know deepfakes are difficult to identify with our naked eyes, so
human sight error can be eliminated.
• User-Friendly interface.
• Effective Prediction System.
LIMITATIONS:
• One of the most important challenges facing the researchers is the lack
of high-quality dataset.
• deep learning models often require large dataset for the training step in
order to produce good results, which are not freely accessible or need
permission from social media providers.
• The rapid development of deepfake GAN models can also bring a new
challenge where unseen types of generated images and video may not
be discovered by the current deep learning models
APPLICATIONS
 Using Machine Learning models, we will be able to predict whether
the given data is fake or real.
 The model firsts use a deep learning network to extract face
features based on face recognition networks. Then, a fine-tuning
step is used to make face features suitable for real/fake image
detection.
 This deepfake image detection system can be used in many sectors,
including social media companies, security organizations, and news
agencies
FUTURE WORK
1. Improving Learning
videos/images.
model
for better prediction
of
fake
2. Although deep learning has shown a remarkable performance in
deepfakes detection, the quality of deepfake has been increasing.
Hence, the current deep learning methods need to improve as well
to successfully identify fake videos and images
3. The current deep learning methods, there is not a clear method to
know the number of layers needed and which architecture is
appropriate for deepfake detection.
CONCLUSION
Deepfake had become popular due to the massive availability of
images and videos in social content. This is particularly important
nowadays because the tools for making deepfakes are becoming
more accessible, and social media sites will easily allow people to
distribute and share such fake content. In this project, we describe a
new deep learning-based method that can effectively distinguish AIgenerated fake videos (DeepFake Videos) from real videos. Our
method is based on the observations that the current DeepFake
algorithm can only generate images of limited resolutions, which are
then needed to be further transformed to match the faces to be
replaced in the source video. Such transforms leave certain
distinctive artifacts in the resulting DeepFake Videos, which can be
effectively captured by a dedicated deep neural network model. We
evaluate our method on several different sets of available DeepFake
Videos which demonstrate its effectiveness in practice.
REFRENCES
[1]https://ayushbasral.medium.com/deepfake-detection-using-resnxt-and-lstm-bcc08c086f84.
[2]https://lab.irt.de/a-system-for-deepfake-detection-dfirt/ .
[3]FaceForensics++: Learning to Detect Manipulated Facial Images by Andreas Rossler ¨ 1 Davide
Cozzolino2 Luisa Verdoliva2 Christian Riess3 Justus Thies1 Matthias Nießner1.
[4]Deepfake Video Detection Using Recurrent Neural Networks David Guera Edward J. Delp ¨ Video
and Image Processing Laboratory (VIPER), Purdue Universit.
[5]Digital Forensics and Analysis of Deepfake Videos Mousa Tayseer Jafar Mohammad Ababneh
Mohammad Al-Zoube Ammar Elhassan.
[6] Review of Deepfake Detection Techniques by M P Adithya Vijayan, Thushara P, Sanjana S, Karthik P
C.
[7] Deepfake Detection using ResNxt and LSTM _ by Ayush Basral _ Medium.
Thank You
