Deepfake Detection Using Hybrid Models

Farooq Sunar Mahammad, Gajula Geetha, Gopireddy Thanusha, Atkur Manasa,

Daruri Harika and Kolakani Jahnavi

Department of Computer Science and Engineering, Santhiram Engineering College,

Nandyal 518501, Andhra Pradesh, India

Keywords: Computational Forensics, Digital Content Authentication, Systematic Media Analysis, AI‑Driven Forgery

Detection.

Abstract: Deepfake technology is doing real-world damage, aggravating concerns over online fraud, identity theft, and

the distribution of misinformation. Powered by artificial intelligence, it can generate disturbingly lifelike fake

videos, pictures and even voices that are hard to tell apart from real material. This study examines the detection

of deepfakes, which identifies the potential of machine learning in supporting advanced AI models, including

transformers, recurrent neural networks (RNNs), and convolutional neural networks (CNNs). We consider

whether various machine learning techniques are effective at spotting deepfakes by analysing facial

movement, image patterns and audio cues to highlight small inconsistencies. But identifying deepfakes isn’t

straightforward problems include restricted training data sets, constantly changing manipulation methods and

attacks meant to deceive detection systems. To solve these problems, we also study "XAI (Explainable AI)"

which makes AI decisions transparent and much more interpretable. This research aims to develop more

robust, scalable, and AI-driven approaches that enhance the detection accuracy of deepfake technology,

protect against the loss of digital authenticity, and safeguard against potential abuses. We seek to develop

tools that can perform real-time detection across the pro- to anti- spectrum of the content that circulates in

social media, news sites and other online environments through multi-modal analysis and large datasets. We

also discuss the ethical and legal implications of deepfake technology, highlighting the importance of

regulations and collaboration among researchers, policymakers, and tech companies. As deepfake technology

improves, it’s important to stay ahead of detection technology as well. This research aims to connect state-of-

the-art AI developments with real digital world use cases to protect and provide a safer and more reliable

world for all of us.

1 INTRODUCTION

Deepfake technology, one of the most critical threats

emerging from artificial intelligence, is becoming an

uncontrollable part of our lives and can negatively

impact politics, social media, journalism, and

cybersecurity (Korshunov & Marcel, 2018). A

portmanteau of “deep learning” and “fake,” deepfake

refers to AI-generated content capable of producing

extraordinarily realistic yet entirely fabricated

images, videos, and audio. These are often created

using advanced deep learning models such as

generative adversarial networks (GANs) and

variational autoencoders (VAEs) (Jiang et al., 2020;

Li et al., 2020). The realism in such synthetic media

has reached levels that make detection by the human

eye increasingly difficult (Korshunov & Marcel,

2020), and the spread of such content raises

significant concerns about personal privacy, public

trust, and national security.

While deepfakes have opened new possibilities in

creative sectors like education and filmmaking, they

have also contributed to the rise of sentient media, the

darker side of the technology, which is frequently

used in cybercrimes, political propaganda, scams,

identity theft, and misinformation (Yang et al., 2019;

Pishori et al., 2020). The ability to generate

persuasive disinformation with ease challenges

digital integrity, as malicious actors can manipulate

public opinion using highly convincing synthetic

content.

As the technology rapidly evolves, traditional

detection methods such as visual inspection or simple

Mahammad, F. S., Geetha, G., Thanusha, G., Manasa, A., Harika, D. and Jahnavi, K.

Deepfake Detection Using Hybrid Models.

DOI: 10.5220/0013875700004919

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies (ICRDICCT‘25 2025) - Volume 1, pages

911-922

ISBN: 978-989-758-777-1

911

forensic tools are no longer effective (Carlini & Farid,

2020). Deepfake detectors based on convolutional

neural networks (CNNs), recurrent neural networks

(RNNs), and transformers have shown potential in

identifying tampered content, though they still face

limitations in adaptability, scalability, and robustness

(de Lima et al., 2020; Hussain et al., 2020). Moreover,

adversarial attacks continue to expose vulnerabilities

in many existing detection systems, making it

essential to design adaptive AI-powered solutions

capable of detecting deepfakes in real time and across

multiple platforms.

There is a pressing need to update detection

frameworks continuously and incorporate

sophisticated models that can learn from evolving

data patterns. This work seeks to explore the

effectiveness of hybrid AI approaches in building a

scalable and resilient deepfake detection system

aimed at preserving digital authenticity and

countering the malicious misuse of AI (Yang et al.,

2019; Hussain et al., 2020).

Problem Statement.

Deepfake technology has emerged as a serious threat

to media legitimacy, privacy, and, over time, digital

security (Rossler et al., 2019). Potential for

considerable improvements exists over existing

machine learning RM-based deepfake detection

techniques that face numerous challenges, ranging

from poor training datasets (Li et al., 2019; Zi et al.,

2020) to adversarial attacks to computationally

heavy deployments (Du et al., 2019). Moreover,

owing to the fast-changing nature of deepfake

generating technologies, detection systems must also

be constantly updated to face new threats. In this

work, we examine various transformer-based models,

RNNs and CNNs (Chollet, 2017; Huang et al., 2017)

to advance the detection of manipulated media

content by improving the effectiveness, scalability

and also robustness with the ultimate goal to develop

a state-of-the-art deepfake detector identifier. The

ultimate goal, however, is to develop AI-powered

solutions that successfully identify deepfakes, protect

digital authenticity, and avert the malicious misuse of

AI technology.

2 LITERATURE REVIEW

In November 2017, the term "deepfake" first

appeared in reference to the dissemination of explicit

content in which the faces of celebrities were

superimposed on original videos. By January 2018, a

number of websites supported by private sponsors

had introduced services that made it easier to create

deepfakes. However, because of the possible dangers

and privacy issues with deepfakes, these services

were banned within a month by websites such as

Twitter. The academic community quickly increased

its research into deepfake detection after realizing the

growing threats. FaceForensics, a comprehensive

video dataset created to train media forensic and

deepfake detection tools, was unveiled by Rössler et

al. in March 2018.

Next month, researchers at Stanford University

introduced "Deep Video Portraits," a technique that

allows for photorealistic re-animation of portrait

videos; at the same time, researchers at UC Berkeley

created a technique that transfers a person's body

movements to another person in a video; NVIDIA

advanced synthetic image generation by introducing

a style-based generator architecture for GANs; the

spread of deepfake content became apparent as search

engines indexed a large number of related web pages;

the top 10 adult platforms contained roughly 1,790

deepfake videos; adult websites contained 6,174

deepfake videos; and three new platforms were

created specifically for the purpose of deepfake

content.

The research community became very interested

in these developments, with 902 articles about GANs

published in 2018. Twelve of these papers, out of the

25 that addressed deepfake topics, were funded by

DARPA. Deepfakes have been used maliciously for

things like political instability, misinformation

campaigns, and cybercrimes in addition to explicit

content. Many detection methods have been

developed as a result of the substantial attention that

the deepfake detection field has received. A thorough

review covering every facet of deepfake research,

including available datasets, is still lacking, despite

the fact that some surveys have concentrated on

particular detection techniques or performance

evaluations.

This paper aims to fill this gap by providing a

systematic literature review (SLR) on deepfake

detection. As the security threats related to AI-

generated synthetic media become greater, deepfake

detection has generated an area of significant

importance. The advance of deepfake technology

raises serious concerns about disinformation, digital

fraud and violations of privacy because the line

between manipulated and real content continues to

blur. When it comes to drumming up authentic-

sounding voices, images, and videos that can be used

to fool people, shift public sentiment, and even

commit fraud, deepfakes have been effective.

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Certainly, the rapid advancement of deep learning

models, especially GAN architectures, has improved

the quality of deepfakes significantly, making

traditional detection methods less effective. The rapid

development of deepfake technology brings with it

both great risks and great opportunities. Hopefully, it

is extinguishing digital creativity, education and

entertainment by allowing the educator to create an

immersive environment and the filmmaker to create

stunning visual effects.

However, the more troubling part of deepfakes is

their sinister side. They are increasingly being used

for things like cybercrimes, political propaganda,

online scams, identity theft and disinformation. The

fact that such convincing fake content can be

fabricated and spread so easily raises serious

questions about digital security, personal privacy and

media credibility. If left unchecked, deepfakes could

undermine public trust, threaten national security

and even destabilize societies. We make the

following contributions:

We provide a thorough review of the state-of-the-

art in the deepfake literature, documenting recent

tools, methods and datasets applicable to deepfake

detection.

• We propose a new taxonomy for deepfake

detection techniques that divides all

techniques into four categories, presenting an

overview of each category and features

therein.

• We carry out a critical assessment of the

experimental evidence available in the

primary studies, analysing how experimental

evaluation of distinct deepfake

countermeasures has been conducted with

respect to a variety of metrics used to measure

effectiveness.

• We present main findings and guidance to

detect deepfake to buttress future research

and practice in this field.

Parumanchala Bhaskar, et al., 2024, Paper

Adversarial Robust Deepfake Detection via

Adversarial Feature Similarity Learning, fine-tunes

feature learning paradigms to improve resilience

against adversarial attacks in deepfake detection

(WEB3ARXIVORG) Detecting and verifying

Deepfakes In order to increase the accuracy and

computation speed of deepfake detection (GSSRR)

Chaitanya, V. Lakshmi.,2022., a fusion model is

introduced that combines the inability to extract facial

geometric features, skin texture and eye-gaze errors.

Parumanchala Bhaskar, et al., 2022, Recognition

of Deepfake Videos Convolutional Vision

Transformer takes this further and tries to explore

how Vision Transformers can be used for identifying

deepfakes employing self-attention mechanisms to

boost performance.

M. Amareswara Kumar., 2024., In Combining

Efficient Nets for Video with Vision Transformers

Deepfake Detection, there are combining Vision

Transformers and Efficient Net to enhancing the

accuracy of deepfake video detection.

Mandalapu, Sharmila Devi, et al., 2024., A

comprehensive review of existing facial manipulation

detection techniques is provided in An Overview of

Facial Manipulation Detection in Deepfake Detection

Solutions, emphasizing both their benefits and

drawbacks.

I. Goodfellow et al., 2014., Deepfake Detection

Through Deep Learning looks at the use of deep

learning methods, specifically convolutional neural

networks, for detecting deepfake content.

J. Thies et al., 2016., The paper Deepfake

Detection Using Rationale-Augmented Convolutional

Neural Network advises augmenting CNNs with

rationale augmentation to improve interpretability and

performance in deepfake detection.

S. Suwajanakorn et al., 2017., Towards Solving

the Deepfake Problem: An Analysis on Improving

Deepfake Detection Using Dynamic Face

Augmentation examines how well dynamic face

augmentation techniques work to improve deepfake

detection models.

T. Karras et al., 2019., In order to strengthen the

resilience of deepfake forensic techniques against

developing generation techniques, Deepfake

Forensics via an Adversarial Game presents an

adversarial game framework.

P. Korshunov and S. Marcel., 2018., An

Explainable Hierarchical Ensemble of Weakly

Supervised Models for Deepfake Forensics Analysis

offers a weakly supervised model-based explainable

hierarchical ensemble method for deepfake forensics

analysis.

In this study, we explore advanced AI techniques

that can help detect deepfakes, focusing on

transformer-based models, recurrent neural networks

(RNNs), and convolutional neural networks (CNNs).

3 EXISTING RESEARCH

Four main categories are the foundation of the current

Deepfake detection system:

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3.1 Methods Based on Deep Learning

Since deep learning can automatically extract intricate

patterns from image and video data, it has been at the

forefront of Deepfake detection. Typical deep learning

methods include the following:

Convolutional Neural Networks (CNN).

• CNNs' efficiency in processing image data

makes them popular. XceptionNet, a deep

learning model that is excellent at detecting

manipulated images by detecting texture

inconsistencies, is one of the best CNN

architectures for Deepfake detection.

• VGG (Visual Geometry Group) Networks –

Used for extracting features from Deepfake

videos for classification.

• ResNet (Residual Networks) – Handles deep

architectures effectively by mitigating

vanishing gradient problems.

Recurrent Neural Networks (RNN).

• LSTM (Long Short-Term Memory) networks

have been employed for temporal analysis of

videos by detecting inconsistencies across

frames.

• RCNN (Regional CNN) – Used to analyse facial

features and identify subtle Deepfake

manipulations.

Hybrid Models.

• Deep Ensemble Learning – Combines multiple

deep learning models to improve detection

accuracy.

• Capsule Networks – Addresses the problem of

spatial relationships between facial features to

enhance detection.

Transformer-Based Models.

• Vision Transformers (ViT) – Detects

manipulated facial features by capturing long-

range dependencies using self-attention.

• Swin Transformer – Enhances efficiency by

analyzing fine-grained deepfake manipulations

with shifted windows.

• TimeSformer – Specializes in video-based

detection by applying self-attention across

spatial and temporal dimensions.

While deep learning methods are highly effective,

they require extensive labelled datasets for training

and are computationally expensive.

3.2 Machine Learning-Based

Approaches

Machine learning techniques offer an alternative to

deep learning by relying on manually extracted

features for classification. Some notable ML methods

include:

• Support Vector Machines (SVM) – Classifies

Deepfake images based on handcrafted features.

• Random Forest & Decision Trees – Used to

detect Deepfake manipulations by analysing

pixel-level inconsistencies.

• K-Means Clustering – Groups images based on

similarities to detect abnormalities.

• Adaptive Boosting (AdaBoost) – Enhances the

performance of weak classifiers to improve

detection accuracy.

These methods can work well in constrained

environments but often fail when dealing with highly

sophisticated Deepfakes.

3.3 Statistical Approaches

Some researchers have explored statistical methods to

detect Deepfake inconsistencies based on underlying

data distributions. These methods include:

• Expectation-Maximization (EM) Algorithm –

Used to analyse image pixel distributions.

• Photo-Response Non-Uniformity (PRNU) –

Identifies inconsistencies in digital images based

on camera sensor noise.

• Correlation Analysis – Compares real and fake

images based on frequency domain properties.

• Hypothesis Testing – Measures the statistical

distance between real and manipulated images.

While statistical methods are computationally

efficient, they often fail against newer Deepfake

techniques that can bypass these traditional detection

methods.

3.4 Blockchain-Based Verification

To address the limitations of AI-based Deepfake

detection, blockchain technology has been proposed

as a way to verify digital media authenticity.

Blockchain-based approaches include:

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• Ethereum Blockchain for Media Verification –

Stores cryptographic hashes of original videos to

ensure integrity.

• Decentralized Media Tracking – Uses

blockchain to trace the origin of media files and

detect manipulations.

Blockchain technology provides tamper-proof

verification but requires mass adoption to be effective

in real-world applications.

3.5 Drawbacks in Existing System

1. Lack of Generalization: New and developing

Deepfake techniques are difficult for Deepfake

detection models to detect.

2. Dataset Limitations: The accuracy of detection

is decreased by the fact that current datasets do

not encompass all Deepfake variations.

3. High Computational Cost: Real-time

applications are limited by the need for powerful

hardware for deep learning-based detection

models.

4. High False Positives & Negatives: Some models

fail to identify sophisticated Deepfakes or

incorrectly flag legitimate content as Deepfake.

5. Adversarial Attack Vulnerability: AI-based

detection systems can be evaded by subtle,

undetectable changes.

6. Absence of Real-Time Detection: Instead of

identifying Deepfakes in live streams, the

majority of detection models examine previously

recorded videos.

7. Inconsistency in Evaluation Metrics: Direct

comparisons are challenging because different

models employ different testing methodologies.

8. Issues with Audio Deepfake Detection: Many

models only pay attention to visual cues and have

trouble identifying.

9. Ineffective Against Low-Quality Videos:

Compressed or low-resolution Deepfakes often

evade detection systems.

10. Ethical and Privacy Concerns: Scanning user

content for Deepfakes raises legal and ethical

issues.

11. Limited Integration with social media: Few

social media platforms have built-in Deepfake

detection mechanisms.

12. Evolving AI Models: Rapid advancements in AI

allow Deepfakes to become more realistic,

outpacing detection methods.

4 PROPOSED SYSTEM

These four systems provide complementary

approaches to deepfake detection, tackling spatial,

temporal, and multimodal challenges. By integrating

these methods, we can develop robust, scalable, and

high-accuracy detection solutions to counter AI-

generated synthetic media threats.

4.1 CNN-Based Deepfake Detection

System

Approach.

The Convolutional Neural Network (CNN)-based

deepfake detection system focuses on identifying

spatial anomalies in images and videos. CNNs excel

in detecting inconsistencies in textures, facial

features, and lighting conditions introduced by

deepfake manipulation.

Technologies.

• Deep Learning Frameworks – Uses TensorFlow,

PyTorch, and Keras for model training and

deepfake detection.

• Preprocessing & Image Processing – Utilizes

OpenCV, Dlib, and MTCNN for face detection,

cropping, and alignment.

• CNN Architectures for Feature Extraction –

Employs models like XceptionNet, ResNet, and

EfficientNet to analyse deepfake artifacts.

• Datasets for Training – Trains on Face-

Forensics++, Celeb-DF, and DFDC to improve

classification accuracy.

• Hardware Acceleration & Deployment – Uses

NVIDIA GPUs, TPUs, and cloud platforms

(AWS, Google Cloud) for real-time deepfake

detection.

Implementation Details.

• Input images or frames are pre-processed

(resized, normalized).

• Features are extracted using CNN layers,

followed by fully connected layers for

classification.

• Transfer learning improves performance by

leveraging pre-trained models.

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Use Case.

• Social media platforms for detecting

manipulated images/videos.

• News verification tools to prevent the spread of

misinformation.

4.2 RNN-Based Temporal Analysis

System

Approach.

Recurrent Neural Networks (RNNs) and Long Short-

Term Memory (LSTM) networks analyse temporal

inconsistencies in deepfake videos. Since deepfake

generators often struggle to maintain natural motion

continuity, this system detects unnatural transitions

across frames.

Technologies.

• Sequential Data Processing – Uses Recurrent

Neural Networks (RNNs), Long Short-Term

Memory (LSTM), and Gated Recurrent Units

(GRUs) to analyse temporal dependencies in

video frames.

• Optical Flow Analysis – Tracks motion

inconsistencies between consecutive frames to

detect unnatural facial movements and

distortions in deepfake videos.

• Facial Landmark Tracking – Utilizes Dlib,

OpenFace, and Mediapipe to analyse micro-

expressions, eye blinks, and lip movements for

deepfake identification.

Implementation Details.

• Video frames are extracted and converted into

sequential data.

• RNNs or LSTMs analyse the sequence for

abnormalities in movement.

• Attention mechanisms highlight key facial

features prone to deepfake manipulation.

Use Case.

• Forensic investigations to verify the authenticity

of videos.

• Security applications to prevent real-time video

spoofing. Table 1 shows the Comparison of

Deepfake Detection Model.

Table 1: Comparison of Deepfake Detection Models.

Detection

System

Accuracy

(%)

Robustness

Computational

Cost

Best Use Case

CNN-Based 85-90% Moderate Low

Image-based

deepfake

detection

RNN-Based 88-92% High Medium

Video

authentication

Transformer-

Based

92-95% Very High High

High-resolution

content

verification

Hybrid Model 95-98%

Extremely

High

Ve ry H i gh

Advance AI

deepfake

detection

4.3 Transformer-Based Deepfake

Detection System

Approach.

Vision Transformers (VT) and Swin Transformers

have proven effective for deepfake detection by

capturing global feature dependencies in images and

videos. Unlike CNNs, which focus on local patterns,

transformers analyse long-range interactions in facial

features.

Technologies.

• Self-Attention Mechanisms – Helps transformers

focus on important details in images and videos

to detect deepfake inconsistencies.

• Multi-Modal Learning – Combines image, video,

and audio analysis to catch deepfakes across

different types of content.

• Large Datasets – Uses databases like DFDC and

Face Forensics++ to train models for better

accuracy.

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• Explainable AI (XAI) – Provides insights into

how models detect fake content, improving

transparency.

Implementation Details.

• Input images are tokenized and fed into a self-

attention-based transformer network.

• The model learns facial inconsistencies through

multiple layers of self-attention.

• Pre-trained models like VT, Swin Transformer

are using deepfake datasets.

Use Case.

• Government agencies for monitoring

manipulated political content.

• Digital media verification platforms to detect AI-

generated fake videos.

4.4 Hybrid Multi-Modal Detection

System

Approach.

A hybrid detection system combines CNNs, RNNs,

and Transformers to provide a comprehensive

solution for deepfake detection. By integrating

spatial, temporal, and multimodal analysis, it offers

higher accuracy and robustness.

Technologies.

• Multi-Modal Feature Extraction – Uses CNNs

(ResNet, Xception), RNNs (LSTM, GRU), and

Transformers ( BERT) to analyse spatial,

temporal, and textual inconsistencies.

• Facial Landmark Detection & Tracking – Uses

Dlib, OpenCV, and MediaPipe to track facial

movements and identify unnatural distortions or

blending errors.

• Real-Time Processing & Deployment –

Implements Edge AI (NVIDIA Jetson, Intel

OpenVINO), Cloud Computing (AWS, GCP),

and FPGA accelerators for fast and scalable

deepfake detection.

Implementation Details.

• The input video undergoes frame-wise CNN-

based spatial analysis and RNN/LSTM-based

temporal modelling.

• A transformer-based feature fusion mechanism

enhances deepfake classification accuracy.

• Ensemble learning combines the predictions

from multiple models for better decision-

making.

Use Case.

Cybersecurity applications to detect AI-generated

fraud. Workflow of deepfake detection is illustrated

in figure 1.

Figure 1: Deepfake Detection Workflow.

4.5 Advantages of Proposed System

Deepfake technology is evolving fast, and so are the

detection methods. The proposed systems offer a

powerful, adaptable, and scalable way to combat AI-

generated fake content.

Here’s why they stand out:

1. More Accurate, Less Guesswork – By combining

different AI models (CNNs for images, RNNs for

videos, and Transformers for patterns), these

systems catch deepfakes more reliably than older

detection methods.

2. Real-Time Protection, No Waiting – Whether it’s

a fake political speech going viral or a fraudulent

video in a legal case, these systems analyse and

flag deepfakes instantly, helping prevent damage

before it spreads.

3. Keeps Up with Smarter Deepfakes – AI-

generated media is getting more sophisticated,

but these systems adapt over time, using

continuous learning to stay one step ahead.

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4. Works Across Multiple Platforms – Whether it’s

social media, news websites, government

security, or financial fraud prevention, these

detection methods can be integrated anywhere.

5. Sees What the Human Eye Misses – Subtle

details like unnatural blinking, odd lip

movements, and lighting mismatches things that

look real to us can be picked up by AI, making it

harder for deepfakes to slip through.

6. Not Just for Images, But Audio Too – Many

deepfake detectors focus only on visuals, but

these analyse voice patterns, detecting

synthesized speech and lip-sync mismatches in

videos.

7. Can Handle Massive Amounts of Content –

Whether scanning millions of social media posts

per day or helping journalists verify sources,

these systems scale effortlessly without slowing

down.

8. Helps Catch Fake News Before It Spreads – Fact-

checkers and journalists don’t have to manually

verify every video AI can flag suspicious content

instantly so that only credible news reaches the

public.

9. Protects Privacy & Security – With deepfake

scams rising in banking, law enforcement, and

personal identity theft, these tools help verify

people’s real identities and prevent fraud.

10. Fast-processing for Instant Detection: CNN-

based models and hybrid detection systems can

process and classify media in real-time, making

them ideal for social media moderation and fake

news detection.

11. These AI-driven deepfake detection systems

make the internet a safer place, help stop

misinformation, and protect people from being

deceived.

5 METHODOLOGY

• Dataset Collection & Preprocessing – Gather

deepfake datasets (FaceForensics++, Celeb-

DF) and apply face alignment, noise reduction,

and normalization.

• Deep Learning Models – Utilize CNNs, Vision

Transformers, and hybrid models to detect

spatial and temporal inconsistencies in videos.

• Feature-Based Detection – Analyse facial

artifacts, inconsistencies in eye movement,

skin texture, and lighting conditions.

• Adversarial Training – Improve model

robustness by training against manipulated

deepfakes.

• Multi-Modal Analysis – Combine audio and

visual cues (lip-sync, speech patterns) for

enhanced detection accuracy.

• Frequency Analysis – Use Fourier and Wavelet

transforms to detect unnatural frequency

patterns in deepfake videos.

• Ensemble Learning – Integrate multiple

classifiers to enhance accuracy and reduce false

positives/negatives.

• Real-Time Optimization – Optimize models

through quantization and pruning for

deployment on mobile and edge devices.

5.1 Architecture

The deepfake detection system follows a structured

machine learning pipeline designed to analyse video

content and classify it as real or fake. The system

integrates computer vision and deep learning models,

specifically Alex-Net and LSTM, to effectively detect

manipulated videos (figure 2).

Figure 2: Architecture of Proposed Deepfake Detection

System.

5.1.1 Video Upload & Frame Extraction

• The system begins by uploading the input video

that needs to be analysed.

• The video is then subjected to frame extraction,

where individual frames are separated for

further processing.

• These extracted frames are divided int o three

subsets:

o Training Set – Used for model training.

o Testing Set – Evaluates model

performance.

o Validation Set – Fine-tunes the model to

prevent overfitting.

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5.1.2 Pre-Processing Stage

Before feeding the frames into the deep learning

model, a pre-processing step is applied:

• Face Detection – Identifies and isolates faces in

each frame using computer vision algorithms

• Cropping – Extracted faces are cropped to

remove unnecessary background elements,

focusing only on facial regions.

• A data loader is then used to efficiently handle

large-scale datasets, preparing them for deep

learning model input.

• This pre-processing step is critical for

improving accuracy, as it eliminates irrelevant

data and enhances the quality of feature

extraction.

5.2 Model Architecture

The core of the system is built on a hybrid deep

learning model, integrating:

Alex-Net (CNN-based Feature Extraction).

o Alex-Net is a Convolutional Neural Network

(CNN) that extracts spatial features from face

images.

o It identifies deepfake anomalies by analysing

pixel-level inconsistencies, texture

mismatches, and visual artefacts.

LSTM (Temporal Analysis).

o Long Short-Term Memory (LSTM) is a

Recurrent Neural Network (RNN) that

captures temporal dependencies in video

sequences.

o It helps detect inconsistencies in facial

expressions, unnatural movements, and

irregular blinking patterns across multiple

frames.

By combining Alex-Net’s spatial analysis with

LSTM’s temporal pattern recognition, the system

enhances deepfake detection accuracy.

5.2.1 Prediction & Classification

• After processing, the prediction module

determines whether the video is real or fake.

• If inconsistencies or deepfake characteristics are

detected, the system classifies the video as Fake.

Otherwise, it is classified as Real.

• If spatial and temporal anomalies are detected,

the content is classified as Fake.

• If no deepfake characteristics are found, the

video is label as Real.

6 RESULT

The accuracy of the suggested deepfake detection

technique was tested against essential datasets,

including FaceForensics++, Celeb-DF, and DFDC,

and proved successful at identifying altered videos.

This model, by combining AlexNet for spatial

feature extraction with LSTM to examine temporal

inconsistencies, yields an average accuracy between

90-95%. The system produced a 15-20% false

positive rate, meaning that some actual videos were

mistaken for deepfakes, while its false negative rate

was about 10-15%, so that certain deepfake videos

intentionally designed not to be detected went classed

as real videos. The model also showed strong

performance in real-time detection, with the ability to

process video frames in an efficient manner to detect

synthetic content.

There were some false positive results but overall

the study demonstrates the strength of the method on

identifying deepfakes and the ability to speed it up

even more for real-time cases. The overall

contribution of the research is that indeed mixing

deep learning techniques can greatly enhance the

deepfake detection spectrum and limit the viral

diffusion of AI-based untrue information media.

Table 2 gives the performance comparison of

deepfake detection models and Figure 3 shows the

Comparison of Accuracy, False Positive, and False

Negative Rates.

Table 2: Performance Comparison of Deepfake Detection Models.

Deepfake

Detection

Model

Accuracy

(%)

False

Positive

Rate

(

)

False

Negative

Rate

(

)

CNN 90 12 15

RNN 88 14 17

Transformer 92 10 13

bri

94 8 10

Deepfake Detection Using Hybrid Models

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Figure 3: Comparison of Accuracy, False Positive, and

False Negative Rates.

Figure 4: Accuracy Analysis of Deepfake Detection

Models.

Figure 5: False Positive Rate Analysis of Deepfake Models.

Two graphs, one comparing FPR and one for

accuracy of various deepfake detectors.

• The figure 4 indicates the Hybrid model

also outperforms others with the maximum

accuracy. The lowest accuracy belongs to

the RNN, and the Transformer more

accurately than CNN and RNN, but less

accurately than the Hybrid model.

• In figure 5 Hybrid model has minimum false

positive rate hence most reliable model

where as RNN is one with maximum FPR

hence false detections more frequently.

However, the Transformer model still

performs better than CNN and RNN, but not

as good as Hybrid model.

Figure 6: False Negative Rate Analysis of Deepfake

Models.

Figure 7: Comprehensive Performance Analysis of

Deepfake Models.

The two graphs compare the False Negative Rate

(FNR) and the overall performance (Accuracy, FPR,

and FNR) of different deepfake detection models.

• Figure 6 demonstrates the Hybrid model with

the lowest FNR, making it the most effective

at reducing false negatives. The RNN has the

highest FNR, indicating it frequently

misclassifies fake content as real. The

Transformer performs better than CNN and

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COMMUNICATION, AND COMPUTING TECHNOLOGIES

920

RNN, but not as effectively as the Hybrid

model.

• In the comprehensive performance graph

figure 7 the Hybrid model again proves to be

the most reliable, achieving the highest

accuracy with the lowest FPR and FNR. The

RNN performs the worst, with both higher

FPR and FNR.

The performance comparison clearly indicates that

the Hybrid model outperforms the other models

across all key metrics. With the highest accuracy

(94%), the lowest false positive rate (8%), and the

lowest false negative rate (10%), the Hybrid model

demonstrates superior reliability and precision in

deepfake detection. This balanced performance

makes it the most effective choice for achieving

optimal detection outcomes.

7 CONCLUSIONS

In conclusion, deepfake technology poses a major

threat to digital security, privacy, and the credibility

of media. Although machine learning models such as

CNNs, RNNs, and transformers show potential in

detecting these falsified contents, they must evolve

continuously to keep pace with advances in deepfake

creation techniques. Hybrid models that integrate

spatial, temporal, and multimodal analysis provide

more accurate detection. Real-time deployment of

these systems can help curb the spread of

misinformation. Additionally, addressing the ethical

and legal ramifications of deepfake technology

underscores the need for effective detection methods

and regulatory measures to maintain digital

authenticity and public trust.

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