An Independent Discriminant Network Towards Identification of Counterfeit Images and Videos
Journal:
arXiv
Published Date:
May 30, 2025
Abstract
Rapid spread of false images and videos on online platforms is an emerging
problem. Anyone may add, delete, clone or modify people and entities from an
image using various editing software which are readily available. This
generates false and misleading proof to hide the crime. Now-a-days, these false
and counterfeit images and videos are flooding on the internet. These spread
false information. Many methods are available in literature for detecting those
counterfeit contents but new methods of counterfeiting are also evolving.
Generative Adversarial Networks (GAN) are observed to be one effective method
as it modifies the context and definition of images producing plausible results
via image-to-image translation. This work uses an independent discriminant
network that can identify GAN generated image or video. A discriminant network
has been created using a convolutional neural network based on
InceptionResNetV2. The article also proposes a platform where users can detect
forged images and videos. This proposed work has the potential to help the
forensics domain to detect counterfeit videos and hidden criminal evidence
towards the identification of criminal activities.
