Amplifying The Uncanny
Amplifying The Uncanny
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发表时间：
2020-02
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通讯作者：
Terence Broad;F. Leymarie;M. Grierson
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作者：
Terence Broad;F. Leymarie;M. Grierson
深度神经网络已经非常擅长生成逼真的深度赝品，即（对于未经训练的人来说）与真实图像无法区分的人物图像。 Deepfakes 是由算法生成的，该算法学习区分真实图像和虚假图像，并经过优化以生成系统认为真实的样本。本文以及由此产生的一系列艺术品“被挫败”探索了颠倒这一过程的美学结果，而不是优化系统以生成它预测为假的图像。这最大化了数据的可能性，反过来又放大了这些机器幻觉的神秘本质。
Deep neural networks have become remarkably good at producing realistic deepfakes, images of people that (to the untrained eye) are indistinguishable from real images. Deepfakes are produced by algorithms that learn to distinguish between real and fake images and are optimised to generate samples that the system deems realistic. This paper, and the resulting series of artworks Being Foiled explore the aesthetic outcome of inverting this process, instead optimising the system to generate images that it predicts as being fake. This maximises the unlikelihood of the data and in turn, amplifies the uncanny nature of these machine hallucinations.