The dataset consists of images downloaded from various news websites (New York Times, CNN, Reuters, ABC, PBS, NBCLA, AP News, Sky News, Telegraph, Time, DenverPost, Washington Post, CBC News, Guardian, Herald Sun, Independent, CS Gazette, BBC, Snopes) with several news captions. The dataset was gathered for research purposes only and we do not own copyright neither for the images nor for the captions. The dataset was curated by researchers at TUM.

Please read and agree to the following terms of use in order to access the public test dataset for the MMSys'21 Grand Challenge on Detecting Cheapfakes:

1.)  The Dataset is purely for research purpose and should not be distributed/shared with a third-party.

2.)  Researcher shall use the Dataset only for non-commercial research and educational purposes.

3.) The Technical University of Munich and its contributors make no representations or warranties regarding the Dataset, including but not limited to warranties of non-infringement or fitness for a particular purpose.

4.) Researcher accepts full responsibility for his or her use of the Dataset and shall defend and indemnify the Technical University of Munich and its contributors, including their employees, Trustees, officers and agents, against any and all claims arising from Researcher's use of the Dataset, including but not limited to Researcher's use of any copies of copyrighted Images, Videos, and models that he or she may create from the Dataset.

5.) Researcher may provide research associates and colleagues with access to the Dataset provided that they first agree to be bound by these terms and conditions.

6.) The Technical University of Munich and its contributors reserves the right to terminate Researcher's access to the Database at any time.

7.) If Researcher is employed by a for-profit, commercial entity, Researcher's employer shall also be bound by these terms and conditions, and Researcher hereby represents that he or she is fully authorized to enter into this agreement on behalf of such employer.

8.) The law of Germany or the law of the contributors’ states of incorporation shall apply to all disputes under this agreement.


If you use this dataset, please cite our paper:

@misc{aneja2021cosmos,
      title={COSMOS: Catching Out-of-Context Misinformation with Self-Supervised Learning},
      author={Shivangi Aneja and Chris Bregler and Matthias Nießner},
      year={2021},
      eprint={2101.06278},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}

Note that by agreeing to these terms, you also agree to follow the guidelines provided in the challenge description (https://2021.acmmmsys.org/cheapfake_challenge.php) and not to use any dataset or material other than those explicitly provided for the challenge in building your detector.

Once your request has been approved, we will provide you with the script to download the public test dataset. For any queries, contact us at mmsys21-grandchallenge-cheapfakes@googlegroups.com.

Please fill out your name and surname as they will appear in the submission page (https://mmsys2021challenges.hotcrp.com/).

Please provide your affiliation in full. If you have no affiliation, you can leave a comment in the last form field.

Please provide an e-mail address associated with your affiliation (if available).