![Delhi Police Registers FIR in Deepfake Video Case Involving Actress Rashmika Mandanna](http://media.assettype.com/sinceindependence%2F2023-11%2Ffce00020-f146-434e-b4bf-d128f1b56779%2FWhatsApp_Image_2023_11_11_at_5_58_01_PM.jpeg?w=480&auto=format%2Ccompress&fit=max)
Deepfake Video Case: On November 10, Delhi Police filed an FIR against unidentified individuals in connection with the Deepfake video case involving actress Rashmika Mandanna.
The Delhi Women's Commission had previously issued a notice to the police regarding this matter.
The FIR, registered under Sections 465 and 469 of the IPC (Indian Penal Code) and Sections 66C and 66E of the IT Act, 2000, was filed at the Special Cell Police Station in Delhi.
The police have formed a dedicated team to initiate an investigation into the incident.
Earlier, the Delhi Women's Commission had sent a notice to the police, urging them to conduct a thorough investigation against the accused.
Expressing concern for Rashmika, the Commission highlighted that the actress's image had been misused. The case is of a serious nature, and as of now, no arrests have been made.
Recently, a deepfake video of Rashmika Mandanna was going viral. In reality, it was a video of another girl named Zara Patel, which was edited with the actress's face.
When Rashmika noticed the video, she clarified its falsehood through a social media post, expressing her concerns.
Amitabh Bachchan also shared the video on Twitter, issuing an alert. He spoke out against such fake editing and emphasized the need for strict action.
Besides Big B, many other celebrities were also worried about fake videos and expressed their concerns.
The term 'deepfake' is derived from 'deep learning,' a subset of machine learning. Deepfake technology is based on artificial neural networks and employs multiple layers (hence, 'deep').
It creates content that appears authentic, even though it may not be. The first notable instance of deepfake technology emerged in 2017 when a Reddit user created numerous deepfake videos.
The creation of deepfake videos involves two networks: an encoder and a decoder. The encoder reads the original content and transfers it to the decoder network, which generates the fake video with a different face.
This process results in a video where the face has been replaced, while the rest of the video remains the same.