Call for Participation: ImageCLEF Deepfake Detection and Generation

[Cristian Stanciu]

We invite you to participate in the first edition of the ImageCLEF Deepfake task!

This task aims to investigate state-of-the-art deepfake detection methods and to analyze the factors that make certain deepfakes especially difficult to detect, with a particular emphasis on the generation process itself. To enable this study, the task will be conducted across two modalities: audio and images.

For this year, 2 different sub-tasks aim to challenge participants: The Deepfake Generation task and the Deepfake Detection task. 

For the Generation task, participants are asked to generate Images and Audio files under specific constraints (facial landmarks, identity constraints for Images, constraint phrases, identity similarity for Audio) that can fool state-of-the-art Deepfake Detectors.

For the Detection task, participants will implement deepfake detectors that are robust enough to detect Deepfakes in all kinds of conditions.

ImageCLEF 2026 is an evaluation campaign that is being organized as part of the CLEF (Conference and Labs of the Evaluation Forum) labs. The campaign offers several research tasks that welcome participation from teams around the world.

The results of the campaign appear in the working notes proceedings, published by CEUR Workshop Proceedings (CEUR-WS.org) and are presented in the CLEF conference. Selected contributions among the participants, will be invited for publication in the following year in the Springer Lecture Notes in Computer Science (LNCS) together with the annual lab overviews.

Find out more: https://www.imageclef.org/2026/deepfake-detection-and-generation