1. Detection of Illicit Content in Video Streams Niall Rea & Rozenn Dahyot http://www.pixalert.com/

2. What? Why? By illicit content, we mean pornographic material By illicit content, we mean pornographic material Applications: Applications: Kid protection (Parental control) Kid protection (Parental control) Company protection (company computers scanning) Company protection (company computers scanning) Pedophilia (cyber surveillance) Pedophilia (cyber surveillance)

3. State of the Art Text Analysis (Internet Filtering) Text Analysis (Internet Filtering) Filenames Filenames Text surrounding images Text surrounding images Known URLs Known URLs Image analysis Image analysis Skin detection Skin detection Geometrical constraints + Orientation Geometrical constraints + Orientation Face localisation Face localisation Video analysis Video analysis Current approach: keyframe extraction at regular time intervals and still image analysis Current approach: keyframe extraction at regular time intervals and still image analysis

4. Extension to Illicit Video Detection Using Video information Using Video information Color Color Texture Texture Motion Motion Using Audio information Using Audio information Audio energy (loudness) Audio energy (loudness)

5. Video Analysis Considered 2 approaches exploiting features from the partially decoded MPEG video stream Considered 2 approaches exploiting features from the partially decoded MPEG video stream Smart keyframe selection for real time performance based on macroblock type Smart keyframe selection for real time performance based on macroblock type Exploiting motion vectors for periodic motion detection Exploiting motion vectors for periodic motion detection Optimised open-source ffdshow decoder (extraction of compressed domain motion features from MPEG-1/2/4) Optimised open-source ffdshow decoder (extraction of compressed domain motion features from MPEG-1/2/4) Poesia filter for skin colour detection Poesia filter for skin colour detection

6. Shot cut detection based on ratio of macroblock types of consecutive inter-coded frames in a sub-GOP Shot cut detection based on ratio of macroblock types of consecutive inter-coded frames in a sub-GOP (e.g. shot cut occurs between the first reference frame and the first B-frame) (e.g. shot cut occurs between the first reference frame and the first B-frame) Macroblocks in both B-frames will be heavily backward predicted (indicated by the heavier arrows). A shot cut is deemed to have occurred if and Macroblocks in both B-frames will be heavily backward predicted (indicated by the heavier arrows). A shot cut is deemed to have occurred if and Shot Cut detection Shot cut n-1n-3n-2n PBB I

7. Motion extraction Compressed (MPEG motion vectors) Compressed (MPEG motion vectors) Background/ global motion compensation (Coudray 2004) Background/ global motion compensation (Coudray 2004) 4 parameter motion model 4 parameter motion model Calculate zoom Calculate zoom Calculate translation Calculate translation Assume global motion only occurs in non-skin and reasonably high texture areas Assume global motion only occurs in non-skin and reasonably high texture areas Compute a 2D histogram of those motion vectors Compute a 2D histogram of those motion vectors Global translation is the mode of the histogram Global translation is the mode of the histogram

8. Motion fields When Harry met SallyIllicit video

9. Motion and color segmentation Assume a simple local homogeneous motion field and global homogeneous motion k-means clustering (2 clusters) of motion field Likelihoods computed from Poesia 32^3 bin RGB skin/non skin histograms Priors set empirically

10. Audio stream Content recognition using Audio data Content recognition using Audio data Sport Sport Specificity of illicit: (pseudo) periodicity Specificity of illicit: (pseudo) periodicity Simple feature used: loudness (Audio energy) Simple feature used: loudness (Audio energy) Does not discriminate between different sources of noise (ie voices, specific sounds, etc.) Does not discriminate between different sources of noise (ie voices, specific sounds, etc.) Capture the dominant pattern of the audio data Capture the dominant pattern of the audio data

11. Audio stream Illicit Audio: Scene of When Harry met Sally

12. Loudness Harry and Sally are talking (5s) Sally is faking it (5s) Audio energy computed over a 40ms (duration of an video frame at 25 fps)

13. Periodicity Signal: s(t)=1+sin(t)Signal: s(t)=rand(t) Autocorrelation:

14. Periodicity of audio energy Harry and Sally are talking Sally is faking it Correlation of the loudness computed over 5s

15. Measure of periodicity in audio data Harry and Sally are talkingSally is faking it Measure of periodicity: difference between the surface defined by the maxima and the minima

16. Measure of periodicity in audio data Measure of periodicity over the sequence ‘When Harry met Sally’

17. Other results False alarm rate assessed on 20minutes of non-illicit material (scenes from movies and music videos) False alarm rate assessed on 20minutes of non-illicit material (scenes from movies and music videos) Detection rate assessed on 10 minutes of 8 illicit materials Detection rate assessed on 10 minutes of 8 illicit materials FA: 2% DR: 5 extracts (~9minutes of recordings) are flagged as illicit 3 (~minutes recordings) are missed

18. Future work: Motion and periodicity Periodic Motion on P- frames Periodic Motion on P- frames Mean motion vector over skin regions Mean motion vector over skin regions Correlation between periodic motion and audio? Correlation between periodic motion and audio?

19. Any question