Skip to main content
SpringerLink
Log in

InnerEye: A Tale on Images Filtered Using Instagram Filters - How Do We Interact with them and How Can We Automatically Identify the Extent of Filtering?

  • Conference paper
  • First Online:
Mobile and Ubiquitous Systems: Computing, Networking and Services (MobiQuitous 2022)

  • 277 Accesses

Abstract

Even though digitally filtered images are taking over the Internet for their aesthetic appeal, general people often feel betrayed if they are dealt with filtered images. Our study, comprising a series of structured surveys over images filtered using Instagram filters, reveals that different people perceive the filtered images differently. However, people have a common need for an automated tool to help them distinguish between original and filtered images. Accordingly, we develop an automated tool named ‘InnerEye’, which is capable of identifying how far an image is filtered or not. InnerEye utilizes a novel analytical design of a Neural Network Model that learns from a diverse set of images filtered using Instagram filters. Rigorous objective and subjective evaluations confirm the efficacy of InnerEye in identifying the extent of filtering in the images.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 89.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 119.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Agarwal, S., Varshney, L.R.: Limits of deepfake detection: a robust estimation viewpoint (2019)

    Google Scholar 

  2. Bandura, A.: Social cognitive theory of personality. In: Handbook of Personality, vol. 2, pp. 154–96 (1999)

    Google Scholar 

  3. Belkasoft: Belkasoft forgery detection module (2021). http://reveal-mklab.iti.gr/reveal/

  4. Birajdar, G.K., Mankar, V.H.: Digital image forgery detection using passive techniques: a survey. Digit. Investig. 10(3), 226–245 (2013)

    Article  Google Scholar 

  5. Boididou, C., Papadopoulos, S., Kompatsiaris, Y., Schifferes, S., Newman, N.: Challenges of computational verification in social multimedia. In: Proceedings of the 23rd International Conference on World Wide Web, pp. 743–748 (2014)

    Google Scholar 

  6. Dearden, L.: The fake refugee images that are being used to distort public opinion on asylum seekers. Independent 16(9), 15 (2015)

    MathSciNet  Google Scholar 

  7. Farid, H., Bravo, M.J.: Image forensic analyses that elude the human visual system. In: Media Forensics and Security II, vol. 7541, pp. 52–61. SPIE (2010)

    Google Scholar 

  8. Frank, J., Eisenhofer, T., Lea, S., Fischer, A., Kolossa, D., Holz, T.: Leveraging frequency analysis for deep fake image recognition (2020)

    Google Scholar 

  9. Fridrich, J.A., Soukal, D.B., Lukáš, J.A.: Detection of copy-move forgery in digital images. In: Proceedings of Digital Forensic Research Workshop. Citeseer (2003)

    Google Scholar 

  10. Gallagher, A.C.: Detection of linear and cubic interpolation in jpeg compressed images. In: The 2nd Canadian Conference on Computer and Robot Vision (CRV 2005), pp. 65–72. IEEE (2005)

    Google Scholar 

  11. Gartus, A., Klemer, N., Leder, H.: The effects of visual context and individual differences on perception and evaluation of modern art and graffiti art. Acta Physiol. (Oxf) 156, 64–76 (2015)

    Google Scholar 

  12. Google: Google landmarks dataset (2020). https://www.kaggle.com/google/google-landmarks-dataset

  13. Guo, Y., Cao, X., Zhang, W., Wang, R.: Fake colorized image detection. IEEE Trans. Inf. Forensics Secur. 13(8), 1932–1944 (2018)

    Article  Google Scholar 

  14. Gupta, A., Lamba, H., Kumaraguru, P., Joshi, A.: Faking sandy: characterizing and identifying fake images on twitter during hurricane sandy. In: Proceedings of the 22nd International Conference on World Wide Web, pp. 729–736 (2013)

    Google Scholar 

  15. Huang, X., Liu, M.Y., Belongie, S., Kautz, J.: Multimodal unsupervised image-to-image translation. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 172–189 (2018)

    Google Scholar 

  16. Huh, M., Liu, A., Owens, A., Efros, A.A.: Fighting fake news: image splice detection via learned self-consistency. In: Proceedings of the European Conference on Computer Vision, pp. 101–117 (2018)

    Google Scholar 

  17. Jang-Hee, Y., Kim, Y., Kyoungho, C., Soonyoung, P., Moon, K.Y.: Method and apparatus for determining fake image (2013). US Patent 8,515,124

    Google Scholar 

  18. Kamakura, A.: pilgram 1.1.0 (2019). https://pypi.org/project/pilgram/

  19. Kasra, M., Shen, C., O’Brien, J.F.: Seeing is believing: how people fail to identify fake images on the web. In: Extended Abstracts of the 2018 CHI Conference on Human Factors in Computing Systems, pp. 1–6 (2018)

    Google Scholar 

  20. Kleemans, M., Daalmans, S., Carbaat, I., Anschütz, D.: Picture perfect: the direct effect of manipulated instagram photos on body image in adolescent girls. Media Psychol. 21(1), 93–110 (2018)

    Article  Google Scholar 

  21. Krawetz, D.N.: Fotoforensics (2021). http://fotoforensics.com/

  22. Lee, Y., Lee, J., Lee, Z.: Social influence on technology acceptance behavior: self-identity theory perspective. ACM SIGMIS Database DATABASE Adv. Inf. Syst. 37(2–3), 60–75 (2006)

    Article  Google Scholar 

  23. Lowe-Calverley, E., Grieve, R.: Self-ie love: predictors of image editing intentions on Facebook. Telematics Inform. 35(1), 186–194 (2018)

    Article  Google Scholar 

  24. Luo, W., Huang, J., Qiu, G.: Robust detection of region-duplication forgery in digital image. In: 18th International Conference on Pattern Recognition (ICPR 2006), vol. 4, pp. 746–749. IEEE (2006)

    Google Scholar 

  25. Maigrot, C., Kijak, E., Claveau, V.: Context-aware forgery localization in social-media images: a feature-based approach evaluation. In: 2018 25th IEEE International Conference on Image Processing (ICIP), pp. 545–549. IEEE (2018)

    Google Scholar 

  26. Manovich, L.: Instagram and contemporary image. CUNY, Nova Iorque (2017)

    Google Scholar 

  27. Marques, O.: Innovative Technologies in Everyday Life. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-45699-7

    Book  Google Scholar 

  28. Marra, F., Gragnaniello, D., Cozzolino, D., Verdoliva, L.: Detection of GAN-generated fake images over social networks. In: 2018 IEEE Conference on Multimedia Information Processing and Retrieval (MIPR), pp. 384–389 (2018)

    Google Scholar 

  29. Messieh, N.: How instagram filters work, and can you tell the difference? (2018). https://www.makeuseof.com/tag/instagram-filters-work-can-tell-difference/

  30. Murthy, D., Gross, A., McGarry, M.: Visual social media and big data. Interpreting instagram images posted on twitter. Digit. Cult. Soc. 2(2), 113–134 (2016)

    Google Scholar 

  31. Nguyen, T., Nguyen, C.M., Nguyen, T., Nguyen, D., Nahavandi, S.: Deep learning for deepfakes creation and detection: a survey (2019)

    Google Scholar 

  32. Nightingale, S.J., Wade, K.A., Watson, D.G.: Can people identify original and manipulated photos of real-world scenes? Cogn. Res. Principles Implications 2(1), 1–21 (2017). https://doi.org/10.1186/s41235-017-0067-2

    Article  Google Scholar 

  33. Omnicore: Instagram statistics (2022). https://www.omnicoreagency.com/instagram-statistics/

  34. Papadopoulou, O., Zampoglou, M., Papadopoulos, S., Kompatsiaris, Y.: Web video verification using contextual cues. In: Proceedings of the 2nd International Workshop on Multimedia Forensics and Security, pp. 6–10 (2017)

    Google Scholar 

  35. Rössler, A., Cozzolino, D., Verdoliva, L., Riess, C., Thies, J., Nießner, M.: Faceforensics: a large-scale video dataset for forgery detection in human faces. arXiv (2018)

    Google Scholar 

  36. Russotti, P., Anderson, R.: Digital Photography Best Practices and Workflow Handbook: A Guide to Staying Ahead of the Workflow Curve. Taylor & Francis, Milton Park (2010)

    Google Scholar 

  37. Statista: Instagram number of daily active instagram stories statistics (2021). https://www.statista.com/statistics/730315/instagram-stories-dau/

  38. Statista: Instagram number of monthly active user statistics (2021). https://www.statista.com/statistics/253577/number-of-monthly-active-instagram-users/

  39. Tariq, S., Lee, S., Kim, H., Shin, Y., Woo, S.S.: Detecting both machine and human created fake face images in the wild. In: Proceedings of the 2nd International Workshop on Multimedia Privacy and Security, pp. 81–87 (2018)

    Google Scholar 

  40. Wagner, J.: Forensically (2021). https://29a.ch/photo-forensics/

  41. Wu, Y., AbdAlmageed, W., Natarajan, P.: Mantra-net: manipulation tracing network for detection and localization of image forgeries with anomalous features. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 9543–9552 (2019)

    Google Scholar 

  42. Xuan, X., Peng, B., Wang, W., Dong, J.: On the generalization of GAN image forensics. In: Sun, Z., He, R., Feng, J., Shan, S., Guo, Z. (eds.) CCBR 2019. LNCS, vol. 11818, pp. 134–141. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-31456-9_15

    Chapter  Google Scholar 

  43. Zampoglou, M.: Reveal - image verification assistant (2021). http://reveal-mklab.iti.gr/reveal/

  44. Zhou, P., Han, X., Morariu, V.I., Davis, L.S.: Learning rich features for image manipulation detection. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, vol. 9, pp. 1053–1061 (2018)

    Google Scholar 

Download references

Acknowledgements

The work was conducted at and supported by the Bangladesh University of Engineering and Technology (BUET), Dhaka, Bangladesh.

Author information

Authors and Affiliations

  1. Bangladesh University of Engineering and Technology, Dhaka, Bangladesh

    Gazi Abdur Rakib, Rudaiba Adnin, Shekh Ahammed Adnan Bashir, Chashi Mahiul Islam, Abir Mohammad Turza, Saad Manzur, Monowar Anjum Rashik, Abdus Salam Azad, Sydur Rahaman, Muhammad Rayhan Shikder & A. B. M. Alim Al Islam

  2. University of California, Berkeley, USA

    Abdus Salam Azad

  3. Microsoft, Redmond, USA

    Tusher Chakraborty

  4. University of Toronto, Toronto, Canada

    Syed Ishtiaque Ahmed

Authors
  1. Gazi Abdur Rakib
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Rudaiba Adnin
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Shekh Ahammed Adnan Bashir
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Chashi Mahiul Islam
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Abir Mohammad Turza
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Saad Manzur
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Monowar Anjum Rashik
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Abdus Salam Azad
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Tusher Chakraborty
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Sydur Rahaman
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Muhammad Rayhan Shikder
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Syed Ishtiaque Ahmed
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. A. B. M. Alim Al Islam
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Rudaiba Adnin .

Editor information

Editors and Affiliations

  1. University of Pittsburgh, Pittsburgh, PA, USA

    Shangguan Longfei

  2. Microsoft Research, Redmond, WA, USA

    Priyantha Bodhi

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Rakib, G.A. et al. (2023). InnerEye: A Tale on Images Filtered Using Instagram Filters - How Do We Interact with them and How Can We Automatically Identify the Extent of Filtering?. In: Longfei, S., Bodhi, P. (eds) Mobile and Ubiquitous Systems: Computing, Networking and Services. MobiQuitous 2022. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 492. Springer, Cham. https://doi.org/10.1007/978-3-031-34776-4_26

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-34776-4_26

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-34775-7

  • Online ISBN: 978-3-031-34776-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation