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利用深度学习识别人工智能生成的艺术作品.docx
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利用深度学习识别人工智能生成的艺术作品.docx
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Identifying AI-Generated Art with Deep Learning
Tommaso Bianco, Giovanna Castellano, Raffaele Scaringi
∗
and Gennaro Vessio
Department
of
Computer
Science,
University
of
Bari
Aldo
Moro,
Bari,
Italy
Abstract
Generative AI, mainly through Diffusion Models, has revolutionized art creation, blurring the distinction
between human and AI-generated art. This study introduces a novel dataset comprising human-made and
AI-generated art and employs Deep Learning models (VGG-19, ResNet-50, ViT) to distinguish between
them. We also use eXplainable AI techniques to derive insights. Our results highlight the potential of AI
to detect machine-generated content, with implications for art authentication.
Keywords
Computer vision, Deep learning, Digital humanities, Generative AI, Synthetic art
1.
Introduction
Art has undergone a profound transformation with the emergence of generative Artificial
Intelligence, notably driven by technologies such as Generative Adversarial Networks [1] and
the increasingly popular Diffusion Models [2]. These groundbreaking innovations have pushed
the boundaries of artistic creation, empowering machines to produce remarkably lifelike images,
including paintings, that challenge our conventional notions of human creativity. Indeed,
generative AI has exhibited the capability to generate synthetic paintings that closely emulate
renowned artists’ styles, brushwork, and aesthetics. This level of fidelity in replicating the
artistic process blurs the demarcation between traditional, human-crafted art and machine-
generated creations.
The distinction between genuine human-made art and its synthetic counterparts carries ex-
tensive implications, influencing aspects such as art authentication, valuation, and preservation
while igniting debates concerning technology’s role in the creative process. While conven-
tional methods of art connoisseurship traditionally relied on expert human judgment, the rapid
evolution of Deep Learning models and the availability of extensive art datasets present new
avenues for addressing this challenge. One intriguing approach to detecting instances gener-
ated by machines, in fact, involves leveraging the capabilities of machines themselves. This
concept is rooted in the idea that the same AI technologies responsible for creating synthetic
content can also be employed for their detection and differentiation from authentic human-made
counterparts. Deep Learning and Computer Vision algorithms can also be trained on large
datasets containing authentic and AI-generated examples. These models can learn to identify
CREAI 2023:
2nd Workshop on Artificial Intelligence and Creativity, November 6–9, 2023, Rome, Italy
∗
Corresponding author.
Envelope-Open
t.bianco5@studenti.uniba.it (T.
Bianco); giovanna.castellano@uniba.it (G.
Castellano); raffaele.scaringi@uniba.it
(R. Scaringi); gennaro.vessio@uniba.it (G. Vessio)
Orcid
0000−0002−6489−8628
(G.
Castellano);
0000−0001−7512−7661
(R.
Scaringi);
0000−0002−0883−2691
(G.
Vessio)
© 2023 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR
Workshop
Proceedings
(CEUR-WS.org)
CEUR
Workshop
Proceedings
http://ceur-ws.org
ISSN 1613-0073
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