With the rapid evolution of AI Generated Content (AIGC), forged images
produced through this technology are inherently more deceptive and require less
human intervention compared to traditional Computer-generated Graphics (CG).
However, owing to the disparities between CG and AIGC, conventional CG
detection methods tend to be inadequate in identifying AIGC-produced images. To
address this issue, our research concentrates on the text-to-image generation
process in AIGC. Initially, we first assemble two text-to-image databases
utilizing two distinct AI systems, DALLE2 and DreamStudio. Aiming to
holistically capture the inherent anomalies produced by AIGC, we develope a
robust dual-stream network comprised of a residual stream and a content stream.
The former employs the Spatial Rich Model (SRM) to meticulously extract various
texture information from images, while the latter seeks to capture additional
forged traces in low frequency, thereby extracting complementary information
that the residual stream may overlook. To enhance the information exchange
between these two streams, we incorporate a cross multi-head attention
mechanism. Numerous comparative experiments are performed on both databases,
and the results show that our detection method consistently outperforms
traditional CG detection techniques across a range of image resolutions.
Moreover, our method exhibits superior performance through a series of
robustness tests and cross-database experiments. When applied to widely
recognized traditional CG benchmarks such as SPL2018 and DsTok, our approach
significantly exceeds the capabilities of other existing methods in the field
of CG detection.

本研究提出了一种使用文本到图像生成过程中的残差网络和内容网络结合的检测方法以及跨多头注意机制来检测 AI 产生的虚假图片，并取得了优于传统 CG 检测技术的结果。