Verifying Multimedia Use at MediaEval 2016
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The document discusses the challenges of verifying multimedia content in the context of media-related events, focusing on a task to classify images as fake, real, or unknown based on metadata and their integrity. It presents a typology of 'fakes', methodologies for gathering verification data, and the results of various teams' submissions in a competition aimed at improving information accuracy online. The study highlights the significance of new evaluation metrics and potential future improvements in the verification process for social media posts.
Verifying Multimedia Use at MediaEval 2016
- 1. Verifying Multimedia Use at MediaEval 2016 Christina Boididou1, Stuart E. Middleton5, Symeon Papadopoulos1, Duc-Tien Dang-Nguyen2,3, Giulia Boato2, Michael Riegler4 & Yiannis Kompatsiaris1 1 Information Technologies Institute (ITI), CERTH, Greece 2 University of Trento, Italy. 3 Insight Centre for Data Analytics at Dublin City University, Ireland. 4 Simula Research Lab, Norway. 5 University of Southampton IT Innovation Centre, UK.
- 2. REAL OR FAKE The verification problem 1
- 3. Real photo Captured in Dublin’s Olympia Theatre A photo of Eagles of Death Metal in concert But Mislabeled on social media as showing the crowd at the Bataclan theatre just before gunmen began firing.
- 4. A TYPOLOGY OF FAKE: REPOSTING OF REAL Photos from past events reposted as being associated to current event ‘Eiffel Tower lights up in solidarity with Pakistan’ ‘Syrian refugee girl selling gum in Jordan’
- 5. A TYPOLOGY OF FAKE: PHOTOSHOPPING Digitally manipulated photos / Tampered ‘Sharks in New York during Hurricane Sandy’ ‘Sikh man is a suspect of Paris attacks’
- 7. MAIN TASK POST IMAGE MEDIAEVAL SYSTEM FAKE REAL AUTHOR (PROFILE) ‘Given a post (image + metadata), return a decision (fake, real, unknown) on whether the information presented by the post reflects the reality’
- 8. SUB-TASK Given an image, return a decision (tampered, non-tampered, unknown) on whether the image has been digitally modified or not. IMAGE MEDIAEVAL SYSTEM TAMPERED NON TAMPERED
- 10. GROUND TRUTH GENERATION Multimedia cases were labeled as fake/real after consulting online reports (articles, blogs) Data (post) collection associated to these cases performed using Topsy (historic events) or using streaming and search API (real-time events) Post set expansion: Near-duplicate image search + journalist debunking reports + human inspection was used to increase the number of associated posts Crowdsourcing campaign carried out with microWorkers platform; each worker asked to provide three cases of multimedia misuse
- 11. DEVELOPMENT SET Event Fake Real Multimedia Posts Multimedia Posts Hurricane Sandy 62 5,559 148 4,664 Boston Marathon bombing 35 189 28 344 Sochi Olympics 26 274 - - MH370 Flight 29 501 - - Bring Back Our Girls 7 131 - - Columbian Chemicals 15 185 - - Passport hoax 2 44 - - Rock Elephant 1 13 - - Underwater bedroom 3 113 - - Livr mobile app 4 9 - - Pig fish 1 14 - - Solar Eclipse 6 137 4 140 Samurai with girl 4 218 - - Nepal Earthquake 21 356 11 1004 Garissa Attack 2 6 2 73 Syrian boy 1 1786 - - Varoufakis 1 61 - - Total 220 9596 193 6225
- 12. TEST SET Event Fake Real MM Posts MM Posts American Soldier Quran 1 17 - - Airstrikes 1 24 - - Attacks in Paris 3 44 22 536 Ankara Explosions - - 3 19 Bush book 1 27 - - Black Lion 1 7 - - Boko Haram 1 31 - - Bowie David 2 24 4 48 Brussels Car Metro 3 41 - - Brussels Explosions 3 69 1 9 Burst in KFC 1 25 - - Convoy Explosion Turkey - - 3 13 Donald Trump Attacker 1 25 - - Eagle Kid 1 334 - - Five Headed Snake 5 6 - - Fuji Lenticular Clouds 1 123 1 53 Total 66 1230 64 998 Event Fake Real MM Posts MM Posts Gandhi Dancing 1 29 - - Half of Everything 9 39 - - Hubble Telescope 1 18 - - Immigrants’ fear 5 33 3 18 ISIS children 2 3 - - John Guevara 1 33 - - Mc Donalds’ Fee 1 6 - - Nazi Submarine 2 11 - - North Korea 2 10 - - Not Afraid 2 32 3 35 Pakistan Explosion 1 53 - - Pope Francis 1 29 - - Protest 1 30 10 34 Refugees 4 35 13 33 Rio Moon 1 33 - - Snowboard Girl 2 14 - - Soldier Stealing 1 1 - - Syrian Children 1 12 1 200 Ukrainian Nazi 1 1 - - Woman 14 children 2 11 - -
- 14. Main task Target class: Fake TASK EVALUATION Sub-task Target class: Tampered Classic IR metrics Precision Recall F1-score -> main evaluation metric Participants were allowed to mark a case as “unknown” (expected to result in reduced recall)
- 15. TASK SUBMISSIONS 10 submissions for the main task 2 submissions for the sub-task (just one team) 3 teams submitted (+1 the organizers)
- 16. TRENDS IN APPROACHES Features being used - Text features (most common) - Post and user metadata - Image forensics - Video quality metadata - Topics of post - Text similarity of posts (per image case) - Trusted sources attributed in text - Mentioned online external sources
- 17. RESULTS: MAIN TASK Team Run Recall Precision F1-Score Linkmedia run1TextKnn 0.9227 0.6397 0.7556 run2CBIR1 0.3406 0.4917 0.4024 run3Sources 0.9463 0.9030 0.9241 run4Fusion 0.9121 0.7525 0.8246 MMLAB@DISI RUN1 0.5487 0.7060 0.6175 RUN2 0.9365 0.8135 0.8707 RUN3 0.9398 0.7405 0.8283 MCGICT hybrid 0.6097 0.7637 0.6781 image 0.5138 0.6975 0.5917 text 0.6292 0.7471 0.6831 VMU Run1 0.8512 0.9812 0.9116 Run2 0.9056 0.7709 0.8328 Run3 0.8869 0.9882 0.9348 Run4 0.8739 0.9799 0.9239 Run5 0.9951 0.5873 0.7386 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 Participants and organizers F1 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 Participants F1
- 18. RESULTS: MAIN TASK Team Run Recall Precision F1-Score Linkmedia run1TextKnn 0.9227 0.6397 0.7556 run2CBIR1 0.3406 0.4917 0.4024 run3Sources 0.9463 0.9030 0.9241 run4Fusion 0.9121 0.7525 0.8246 MMLAB@DISI RUN1 0.5487 0.7060 0.6175 RUN2 0.9365 0.8135 0.8707 RUN3 0.9398 0.7405 0.8283 MCGICT hybrid 0.6097 0.7637 0.6781 image 0.5138 0.6975 0.5917 text 0.6292 0.7471 0.6831 VMU Run1 0.8512 0.9812 0.9116 Run2 0.9056 0.7709 0.8328 Run3 0.8869 0.9882 0.9348 Run4 0.8739 0.9799 0.9239 Run5 0.9951 0.5873 0.7386 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 Participants and organizers F1 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 Participants F1
- 19. RESULTS: MAIN TASK Team Run Recall Precision F1-Score Linkmedia run1TextKnn 0.9227 0.6397 0.7556 run2CBIR1 0.3406 0.4917 0.4024 run3Sources 0.9463 0.9030 0.9241 run4Fusion 0.9121 0.7525 0.8246 MMLAB@DISI RUN1 0.5487 0.7060 0.6175 RUN2 0.9365 0.8135 0.8707 RUN3 0.9398 0.7405 0.8283 MCGICT hybrid 0.6097 0.7637 0.6781 image 0.5138 0.6975 0.5917 text 0.6292 0.7471 0.6831 VMU Run1 0.8512 0.9812 0.9116 Run2 0.9056 0.7709 0.8328 Run3 0.8869 0.9882 0.9348 Run4 0.8739 0.9799 0.9239 Run5 0.9951 0.5873 0.7386 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 Participants and organizers F1 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 Participants F1
- 20. RESULTS: MAIN TASK Team Run Recall Precision F1-Score Linkmedia run1TextKnn 0.9227 0.6397 0.7556 run2CBIR1 0.3406 0.4917 0.4024 run3Sources 0.9463 0.9030 0.9241 run4Fusion 0.9121 0.7525 0.8246 MMLAB@DISI RUN1 0.5487 0.7060 0.6175 RUN2 0.9365 0.8135 0.8707 RUN3 0.9398 0.7405 0.8283 MCGICT hybrid 0.6097 0.7637 0.6781 image 0.5138 0.6975 0.5917 text 0.6292 0.7471 0.6831 VMU Run1 0.8512 0.9812 0.9116 Run2 0.9056 0.7709 0.8328 Run3 0.8869 0.9882 0.9348 Run4 0.8739 0.9799 0.9239 Run5 0.9951 0.5873 0.7386 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 Participants and organizers F1 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 Participants F1
- 21. RESULTS: MAIN TASK Team Run Recall Precision F1-Score Linkmedia run1TextKnn 0.9227 0.6397 0.7556 run2CBIR1 0.3406 0.4917 0.4024 run3Sources 0.9463 0.9030 0.9241 run4Fusion 0.9121 0.7525 0.8246 MMLAB@DISI RUN1 0.5487 0.7060 0.6175 RUN2 0.9365 0.8135 0.8707 RUN3 0.9398 0.7405 0.8283 MCGICT hybrid 0.6097 0.7637 0.6781 image 0.5138 0.6975 0.5917 text 0.6292 0.7471 0.6831 VMU Run1 0.8512 0.9812 0.9116 Run2 0.9056 0.7709 0.8328 Run3 0.8869 0.9882 0.9348 Run4 0.8739 0.9799 0.9239 Run5 0.9951 0.5873 0.7386 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 Participants and organizers F1 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 Participants F1
- 22. RESULTS: SUB-TASK Team Run Recall Precision F1-Score MMLAB@DISI RUN1 0.5 0.4827 0.4912 RUN2 0.9285 0.4906 0.6420 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7
- 23. FUTURE PLANS Reconsider the fake/real distinction Think about different evaluation metrics Use posts from other social media
- 24. Thanks for your attention! ANY QUESTIONS? Get in touch at boididou@iti.gr (@cmpoi) papadop@iti.gr (@sympap) www.revealproject.eu @RevealEU