There's an easy and often-used critique of Jackson Pollock’s iconic drip paintings: anyone—even a kid or a monkey—could splatter color on a canvas and make it look like his work. And it's not entirely off base: sure enough, forgers have taken advantage of the difficulty of determining which canvases are authentic and which aren’t. Just last year, one guy got busted for selling around 60 fake Pollocks to collectors for over $1.9 million. Ouch. 
 
 But, according to many connoisseurs, critics and fakers don’t give the painter enough credit. There are indeed complexities to Pollock's drip art that show it to be the genuine article. And now there’s a computer program helping to make a science out of the deciphering. 
 
 The software uses “computational methods to characterize the low-level numerical differences between original Pollock drip paintings and drip paintings done by others attempting to mimic this signature style,” says Inderscience Publishers. You give it a scan of the possible Pollock, and the program goes to work extracting 4024 numerical image descriptors that the human eye would have trouble deciphering as accurately. 
 
 "The human perception of visual art is a complex cognitive task that involves different processing centers in the brain," explained the program’s creator, Lior Shamir. Yet, a computer can pick up on minute pixel-level features of the art we’re just incapable of seeing. The program isn’t perfect—but, reportedly, it has a pretty solid 93 percent accuracy. And it goes a long way in proving that Jackson Pollock’s creations are unique and not just something any shmoe could throw together. 
 
 A paper on the software will appear in the upcoming issue of International Journal of Arts and Technology, and Shamir has made his program available to anyone interested. Perhaps it will help save a few collectors some big money: in 2006, an authentic “No.5 1948” was sold to a private enthusiast for a staggering $140 million. ||||| Abstract expressionist painter Jackson Pollock was perhaps most famous for his "drip painting" technique. His legacy, however, is plagued by fake "Pollocks" and even experts often have trouble distinguishing the genuine from the counterfeit. Now, a machine vision approach described in a forthcoming issue of International Journal of Arts and Technology has demonstrated 93 percent accuracy in spotting true Pollocks. 
 
 Lior Shamir of Lawrence Technological University in Michigan, USA, was intrigued by the a revolutionary artistic style of dripping paint on a horizontal canvas and has turned to computational methods to characterize the low-level numerical differences between original Pollock drip paintings and drip paintings done by others attempting to mimic this signature style. A scan of a given painting is analyzed and 4024 numerical image descriptors extracted, Shamir explains. Among these descriptors are fractals formed by the movement of the dripping paint and features such as Zernike polynomials and Haralick textures. 
 
 "The human perception of visual art is a complex cognitive task that involves different processing centers in the brain," Shamir explains. "The work of Jackson Pollock showed unique physiological and neurological human responses to Pollock's drip paintings." But, the human eye is limited in its perception of the specific physical qualities of a painting. A computer, on the other hand, can quantify the details at the pixel by pixel level once a painting has been digitized and "see" details and patterns that we do not consciously detect. 
 
 Shamir's analysis demonstrates that although any amateur might imagine they could copy Pollock's work, it is indeed unique and his signature style gave rise to specific features and textures that Pollock pretenders have repeatedly failed to emulate accurately. Shamir points out that his software is publicly available and could be used to analyze the work of other artists in verifying authenticity or revealing the fakes. 
 
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 Shamir, L. (2015) 'What makes a Pollock Pollock: a machine vision approach', Int. J. Arts and Technology, Vol. 8, No. 1, pp.1-10. ||||| Like his work or hate it, it's clear that the painter Jackson Pollock pioneered a distinctive visual style with his drip paintings. If you admire his work (which I do), then you probably admire his distinctive visual flair. If you don't (heathen!), then you probably think that any old schmuck could produce an equivalent work simply by grabbing a paintbrush and having a seizure. 
 
 I am now fortunate enough to be able to argue for my perspective with math on my side. Lior Shamir, a computer scientist at Lawrence Technological University, has taken a series of image analysis algorithms and shown that they can discriminate between real Pollocks and pieces painted in an attempt to mimic his style. 
 
 This isn't the first time that computer science has intruded into the world of fine art. In several cases in the past, it has done so via the art community's invitation, as scientific analysis can help determine whether a suspicious work is likely to be genuine or not. Since Pollock painted very recently, however, it's relatively easy for a forger to match his materials. Whether they match his style or not is debatable given the disagreements noted above about whether there's a distinctive style involved at all. 
 
 There are, however, a number of possible Pollocks that are, as Shamir puts it, of "controversial authenticity," and computer scientists have attempted to use image analysis to weigh in on the issue. The idea that an artist paints in ways that create a distinctive style that's possible to identify based on multiple paintings isn't at all controversial. The question is whether that style can be reduced to a value through the mathematical processing of the information in an image of those paintings. In Pollock's case, past attempts indicated that his paintings have a distinctive fractal nature that can be recognized by algorithms. 
 
 Shamir, however, has developed a software package (originally intended for analysis of medical images) that can best be described as "throw everything at the wall and see what sticks." First, the software runs through a variety of processes that identify basic features of the image such as "textures, colors, edges, shapes, fractals, polynomial decomposition of the image, and statistical distribution of the pixel intensities." Next, it performs various transformations on the image (Fourier transforms, wavelet analysis, and so on), and computes values from those. 
 
 By comparing multiple images from a single source, it identifies the individual computations that provide the most consistent information about the source. Then, given a new image from an unknown source, it performs the same calculations and sees if the results are similar to its training set or not. If they're sufficiently similar, the work is likely to be in the same style as the artist's and probably by the artist him or herself. 
 
 For the Pollock paintings, Shamir obtained 26 paintings known to be by the artist and a second set that was painted by artists who were inspired by Pollock and attempted to create works in his style. The pieces were normalized to contain 640,000 pixels and then divided up into 16 equal-sized areas. Twenty works were used to train the software, then the remaining six were used for testing purposes. The analysis was then repeated multiple times, each with a different set of 20 training images, in order to provide a greater statistical power. The piece was determined to be Pollock or not based on a majority voting system, with each of the 16 sections of the painting getting one "vote." 
 
 Given these paintings as input, the computer was accurate over 90 percent of the time when asked to determine whether a painting was by Pollock or another artist. But previous computer analyses had suggested Pollock's style evolved over time, so Shamir went back and found 26 paintings that were done in the first half of the 1950s. With these as the training set, the accuracy reached 100 percent. 
 
 Looking back at the features that the algorithm ended up using, Shamir is able to identify a number of things beyond fractals that help define Pollock's style. (For the curious, these include Chebyshev statistics, a statistical analysis of the Fourier transform, Haralick textures, and Zernike polynomials taken from other image transforms.) It's not clear whether these are simply different ways of identifying a limited number of stylistic features or whether Pollock's style is multidimensional. 
 
 In any case, it's clear that there's something distinctive there. Whether you like it or not is a separate issue. 
 
 Shamir has placed the source code for this analysis package, termed "Wnd-charm," online. 
 
 International Journal of Art and Technology, 2015. In press; DOI not available.
– How do you tell whether a canvas splattered with paint is the work of Jackson Pollock—or a fake? Collectors have been tricked in the past, but a new computer program could prevent such misfortune, the Smithsonian reports. The software, created by a researcher at Lawrence Technological University, was at first intended to examine medical images, Ars Technica reports. It looks at the paintings from a numerical perspective, making minute examinations that the human eye can't handle, according to a post at Eureka Alert. Once a painting is scanned, the program examines texture, color, shapes, and more. It picks up on 4,024 different numerical attributes of the painting—things like the way dripping paint creates fractals, the press release notes. It's 93% accurate, and anyone can use it: You can download the program right here. What's more, it would seem to prove that even if Pollock's art looks easy to copy, it's actually done in a specialized style, the Smithsonian notes. (Scientific techniques, however, can help to recreate it.)