AI-based multimodal deepfake detection and prevention system
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Date
2026-06
Journal Title
Journal ISSN
Volume Title
Publisher
AIKTC
Abstract
The blistering development of artificial intelligence and deep learning
has provided the possibility to create most convincing synthetic media,
which is often known as deepfakes. There are increasingly greater threats
to privacy, security and digital authenticity that such manipulated images,
videos and audio clips present. This project presents an AI-Based
Multimodal Deepfake Detection and Prevention System that is effective
in detecting and eliminating falsified media through machine learning and
neural network models. The proposed system combines EfficientNet and
ResNet systems to authenticate images and videos and use recurrent neural
networks (RNNs) and spectrogram-based analysis to identify modified
audio. These modules are integrated into a web interface running on Flask,
by which users could upload multimedia contents and automatically check
their authenticity. The system enhances detection accuracy in the various
modalities through pre-processing, feature extraction, and classification.
Such preventative methods as digital watermarking and metadata embedding
are also discussed in order to preserve the reliability of the media.
Altogether, this piece is a step towards building credible digital ecosystems
and combating misinformation that can result due to AI-generated
content.
The project improves the accuracy of the detection of several modalities
by preprocessing, feature extraction, and deep learning classification. Also,
such preventive steps as watermarking and embedding secure metadata
are discussed to decrease the abuse of AI-generated content. This work
helps to empower the integrity of digital media, ethical use of AI, and
protect individuals and organizations against misinformation and identity
distortion.
Key Features :
1. Multi-Mode Detection- Detection of deepfakes in images, videos, etc.
and audio content.