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COSMOS on Steroids: a Cheap Detector for Cheapfakes

Published:22 September 2021Publication History

ABSTRACT

The growing prevalence of visual disinformation has become an important problem to solve nowadays. Cheapfake is a new term used for the altered media generated by non-AI techniques. In their recent COSMOS work, the authors developed a self-supervised training strategy that detected whether different captions for a given image were out-of-context, meaning that even though pointing to the same object(s) in the image, the captions implied different meanings. In this paper, we propose four methods to improve the detection accuracy of COSMOS. These methods range from differential sensing and fake-or-fact checking that detect contradicting or fake captions to object-caption matching and threshold adjustment that modify the baseline algorithm for improved accuracy.

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References

  1. S. Aneja, C. Bregler, and M. Nießner. COSMOS: Catching out-of-context misinformation with self-supervised learning. https://arxiv.org/abs/2101.06278, 2021.Google ScholarGoogle Scholar
  2. S. Aneja, C. Midoglu, D.-T. Dang-Nguyen, M. A. Riegler, P. Halvorsen, M. Niessner, B. Adsumilli, and C. Bregler. MMSys'21 grand challenge on detecting cheapfakes. https://arxiv.org/abs/2107.05297, 2021.Google ScholarGoogle Scholar
  3. D. Cer, Y. Yang, S.-y. Kong, N. Hua, N. Limtiaco, R. St. John, N. Constant, M. Guajardo-Cespedes, S. Yuan, C. Tar, B. Strope, and R. Kurzweil. Universal sentence encoder for English. In Proc. Conf. Empirical Methods in Natural Language Processing: System Demonstrations, pages 169--174, Brussels, Belgium, 2018. Association for Computational Linguistics.Google ScholarGoogle ScholarCross RefCross Ref
  4. P. Fialho, L. Coheur, and P. Quaresma. To BERT or not to BERT dealing with possible bert failures in an entailment task. In Information Processing and Management of Uncertainty in Knowledge-Based Systems, pages 734--747. Springer International Publishing, 2020.Google ScholarGoogle ScholarCross RefCross Ref
  5. K. He, G. Gkioxari, P. Dollár, and R. Girshick. Mask R-CNN. In IEEE Int. Conf. Computer Vision (ICCV), 2017.Google ScholarGoogle Scholar
  6. J. Scott Brennen, F. M. Simon, P. N. Howard, and R. K. Nielsen. Types, sources, and claims of COVID-19 misinformation. [Online] Available: http://www.primaonline.it/wp-content/uploads/2020/04/COVID-19_reuters.pdf. Accessed on July 19, 2021.Google ScholarGoogle Scholar
  7. Y. Mirsky and W. Lee. The creation and detection of deepfakes: A survey. ACM Comput. Surv., 54(1), Jan. 2021.Google ScholarGoogle Scholar
  8. B. Paris and J. Donovan. Deepfakes and Cheap fakes: The Manipulation of Audio and Visual Evidence. [Online] Available: https://datasociety.net/library/deepfakes-and-cheap-fakes/. Accessed on July 19, 2021.Google ScholarGoogle Scholar
  9. N. Reimers and I. Gurevych. Sentence-BERT: Sentence embeddings using Siamese BERT-networks. In Proc. of the 2019 Conf. Empirical Methods in Natural Language Processing and the 9th Int. Joint Conf. Natural Language Processing (EMNLP-IJCNLP), Hong Kong, China, Nov. 2019.Google ScholarGoogle ScholarCross RefCross Ref
  10. N. Schick. Don’t underestimate the cheapfake. [Online] Available: https://www.technologyreview.com/2020/12/22/1015442/cheapfakes-more-political-damage-2020-election-than-deepfakes/. Accessed on July 19, 2021.Google ScholarGoogle Scholar
  11. B. Wang and C.-C. J. Kuo. SBERT-WK: a sentence embedding method by dissecting BERT-based word models. IEEE/ACM Trans. Audio, Speech, and Language Processing, 28:2146--2157, 2020.Google ScholarGoogle ScholarDigital LibraryDigital Library

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  1. COSMOS on Steroids: a Cheap Detector for Cheapfakes

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      • Published in

        cover image ACM Conferences
        MMSys '21: Proceedings of the 12th ACM Multimedia Systems Conference
        June 2021
        254 pages
        ISBN:9781450384346
        DOI:10.1145/3458305

        Copyright © 2021 ACM

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        Publication History

        • Published: 22 September 2021

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        MMSys '21 Paper Acceptance Rate18of55submissions,33%Overall Acceptance Rate176of530submissions,33%

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