Presentation for the 2013 EvoMusArt conference (Evolutionary Music and Art, part of the Evo* conferences on Evolutionary Computation), held in Vienna, Austria. Presents research on the evolution of small programs that perform glitch operations.

1. Evolving Glitch Art
Eelco den Heijer
Vrije Universiteit Amsterdam

2. Outline
• Introduction
• Research Questions
• Glitch Art
• Genotype for Glitch Art
• Fatality rate
• Visual Impact
• Experiments & Results
• Conclusions

3. Introduction
• What is `Glitch'?
• Electronic music
• Circuit bending
• Alter/ `break' digital
circuitry to change sonic
output
• Transfer to visual domain
• `Data bending'

4. Data Bending
• Digital images have specific
encoding
• Differs per file format
• Essence of Visual Data Bending
• Changes in digital encoding
may change the image in
unexpected ways

5. Glitch Art
• Difficult to tell in advance whether
a glitch operation will
• break, or
• change the image
• Like photography
• "Easy to do, but hard
to do well"

6. Research Questions
1. Can we develop a genotype for Glitch
Art? What are the main obstacles?
2. Can we measure, control the fatality rate
of glitch operations?
3. Can we measure, control the visual
impact of glitch operations?
4. Does Evolved glitch art make a new
visual contribution to the EvoArt?

7. Glitch operations
• Manual
• Take any HEX editor
• Open a digital image
• Insert random bytes, save
• Select N bytes, delete them, save
• Replace all 'EF12' by 'FA45', save
• Etc.?

8. Glitch operations

9. Glitch operations
• position and size
• numbers ∈ [0,..,1]
• relative to image size; make them
independent of actual image size
• can be transferred from one image to
another (important in our genotype)

11. Genotype for Glitch
• Our genotype is a 'glitch recipe'
1. a selector of one source image
2. a list of one or more glitch operations

12. Genetic operators
• Tailored for Glitch recipe
• Initialisation
• Crossover
• Mutation

13. Initialisation
• Sample one image from test set of 500
• Test set of famous paintings
• Initialise glitch recipe of 1 to 5
operations

14. Crossover
• Two parents
• Take selector of one of the parents
• One-point crossover on the two lists of
glitch operations
Image
selector
glitch
operations
parent 1 parent 2

15. Crossover examples

16. Mutation (1)
• All parts of the glitch genotype may be
subject to mutation
• Select new image (p=0.1)
• Iterate over all glitch operations
• change operator (p=0.1)

17. Mutation (2)
• Iterate over all glitch operations (cont'd)
• change one or more arguments (p=0.1)
• Numeric: +/- n%, where n∈ [0,..,1]
• Byte array; replace byte with random
new one (p=0.01)
• Byte; +/- n, where n∈ [1,..,4] (result in
[0,..,255]

18. Mutation (3)

19. Fatality rate
• A glitch operation may 'break' the
image (becomes unreadable)
• Some image formats are more
sensitive than others
• Some glitch operations are more
crude than others
• Experiment: calculate fatality rate

20. Fatality Rate: setup
• Take image set of 100, convert to all image
formats (bmp,gif,jpeg,png,raw,tiff)
• Iterate over all image formats
• Iterate over all glitch operations (g)
• Iterate over image set (I)
• I'=g(I)
• If I' is not readable, count as fatality
• Rate = #fatality / #total
• Perform 10 runs

21. Fatality Rate: results

23. Fatility rate: results
• Image Formats
• png is a very sensitive format; unusable
for glitch operations
• bmp,gif,jpeg and raw are most robust
• Operations
• delete is most fatal operation; 53%
overall, 100% on bmp,png,tiff
• not,insert also have high fatality rate

24. Visual impact
• Glitch operation may cause visual
change in resulting image
• Also depends on image format and
glitch operation
• Experiment: calculate visual impact

25. Visual impact: setup
• Setup similar to fatality rate experiment
• Visual impact is calculated using simple
image grayscale distance function
•I'=g(I)
• visual impact = d(I,I')If I' is broken, we
take d=0
• Calculate avg. d for 100 images, 10 runs

26. Visual impact: results

28. Fatality rate: results
• Image format
• gif and raw score high
• png scores lowest (due to high fatality
rate)
• Operations
• replace operator has highest visual
impact (also see GlitchBot)

29. Unsupervised EvoArt
• Evolve glitched images without human
intervention
• Fitness: Simple aesthetic measure
• Based on Global Contrast Factor
(Matkovic et al 2005)
• Calculates difference in hue
• Not specific for Glitch Art

30. Experiment
Population 100
Generations 10
Runs 10
Tournament size 2
Image format gif
Initialisation Custom Glitch Init
Crossover Custom Glitch XO
Mutation Custom Glitch Mut
Fitness Colour Contrast

33. Other results
• Fatality rate of glitch operator act as negative
selection pressure;
• replace most popular (27%)
• delete, insert least popular (7.8% and 7.1%)
• Over 10 runs, over 10 generations, fatality rate
varied between 13 and 20%

34. Conclusions (1)
• It is possible to evolve Glitch images using a
custom Glitch genotype
• Unsupervised, but also for IEC
• The fatality rate in Glitch remains an issue,
but is 'tolerable'
• Choose image format and operations
wisely
• Ensuring Visual impact of glitch operations
also remains an issue

35. Conclusions (2)
• Evolved glitch art :
• Visual addition to EvoArt landscape
• Rough, distorted look 'n feel (punk?)
• Complex image filter
• Image as source
• Pixel displacements
• Not as versatile, or expressive, as S-expressions,
SVG, grammars

36. Future work
• Better tailored aesthetic measure for Glitch art
• Proper image distance functions
• Heuristics based on knowledge on image
formats
• Tune glitch operations
• Perform experiment with single image
• More glitch operations?
• Integrate with other filter techniques
• MOEA

37. Thank you!
Images and paper(s) at
http://www.few.vu.nl/~eelco
Questions?
eelcodenheijer@gmail.com