AUTHENTICITY AND THE POOR IMAGE IN THE AGE OF DEEP LEARNING

Abstract

Deep learning techniques are increasingly used to automate categorization and identification tasks for large datasets of digital photographs. For rasterized images formats, such as JPEGs, GIFs, and PNGs, the analysis happens on the level of individual pixels. Given this, digital images used in deep learning applications are typically restricted to relatively low-resolution formats to conform to the standards of popular pre-trained neural networks. Using Hito Steyerl’s conception of the ‘poor image’ as a theoretical frame, this article investigates the use of these relatively low-resolution images in automated analysis, exploring the ways in which they may be deemed preferable to higher-resolution images for deep learning applications. The poor image is rich in value in this context, as it limits the undesirable ‘noise’ of too much detail. In considering the case of automated art authentication, this article argues that a notion of authenticity is beginning to emerge that raises questions around Walter Benjamin’s often-cited definition in relation to mass image culture. Copies or reproductions are now forming the basis for a new model of authenticity, which exists latently in the formal properties of a digital image.