Deepfake Detection and Generation

Welcome to the 2nd edition of the Deepfake Detection and Generation Task!

Motivation

Deepfakes are AI-generated media that portray real individuals and are often created to manipulate, mislead, or misinform the public. Despite significant efforts by researchers and practitioners to combat the spread of falsified media in recent years, many challenges remain, particularly in understanding how deepfakes are generated and what makes a deepfake easily detectable. While in the past the lack of quality in the generated data made it straight-forward to detect deepfakes, generative models are always evolving, resulting in very realistic deepfakes. One of the most critical issues in this field is the lack of generalization: detection models trained on existing datasets frequently fail when confronted with previously unseen deepfake techniques.

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. This will allow for a more comprehensive evaluation of deepfake characteristics and detection robustness across different forms of media.

The goal of this task is to bring together researchers and practitioners working in the field of disinformation, foster the development of new methods, and gain deeper insights into how deepfakes are generated and detected.

Schedule

The subtasks for Audio and Images will run at the same time.

The two tasks (Generation and Detection) will be carried out sequentially and will not overlap.

• TBD: Registration opens for all ImageCLEF tasks
• TBD: Registration closes for all ImageCLEF tasks
• TBD: Generation task starts. Development dataset released
• TBD: Generation task ends. Deadline for submitting participant runs.
• TBD: Detection task starts. Test dataset released
• TBD: Detection task ends. Deadline for submitting participant runs
• TBD: Submission of participant papers [CEUR-WS]
• TBD: Notification of acceptance
• TBD: CLEF 2027, TBD

Task Description

The task is composed of 3 subtasks, with each one being applied to two modalities: audio or images, and one for multimodal videos with audio. The sub-tasks are strongly connected, with results from one subtask being used in the evaluation of the other. Therefore, the tasks will not run at the same time: the generation task will be held first, and afterwards, the detection task. Although participation in only one subtask or only one modality is allowed, we encourage participation in both sub-tasks, as they are deeply connected. The subtasks are:

Subtask 1: TBD

Subtask 2: TBD

Subtask 3: TBD

Data

Information will be added soon.

Evaluation Methodology

Information will be added soon.

Participant registration

Please refer to the general ImageCLEF registration instructions.

Results

CEUR Working Notes

Citations

Contact

Contact person: Dan-Cristian Stanciu - dan.stanciu1203@upb.ro

Organizers

Image Generation and Detection

Dan-Cristian Stanciu, National University of Science and Technology POLITEHNICA Bucharest
Bogdan Ionescu, National University of Science and Technology POLITEHNICA Bucharest
Liviu-Daniel Ștefan, National University of Science and Technology POLITEHNICA Bucharest
Mihai-Gabriel Constantin, National University of Science and Technology POLITEHNICA Bucharest
Mihai Dogariu, National University of Science and Technology POLITEHNICA Bucharest
Alexandra Andrei, National University of Science and Technology POLITEHNICA Bucharest, Romania

Audio Generation and Detection

Ana Nicolae, National University of Science and Technology POLITEHNICA Bucharest
Andrei-Radu Danila, National University of Science and Technology POLITEHNICA Bucharest
Radu-George Bolborici, National University of Science and Technology POLITEHNICA Bucharest
Marian Negru, National University of Science and Technology POLITEHNICA Bucharest
Alexandru-Florin Ene, National University of Science and Technology POLITEHNICA Bucharest
Ana-Antonia Nicolae, National University of Science and Technology POLITEHNICA Bucharest
Vlad-Mihai Vasilescu, National University of Science and Technology POLITEHNICA Bucharest