  In recent years, methods for producing highly realistic synthetic images have
significantly advanced, allowing the creation of high-quality images from text
prompts that describe the desired content. Even more impressively, Stable
Diffusion (SD) models now provide users with the option of creating synthetic
images in an image-to-image translation fashion, modifying images in the latent
space of advanced autoencoders. This striking evolution, however, brings an
alarming consequence: it is possible to pass an image through SD autoencoders
to reproduce a synthetic copy of the image with high realism and almost no
visual artifacts. This process, known as SD image laundering, can transform
real images into lookalike synthetic ones and risks complicating forensic
analysis for content authenticity verification. Our paper investigates the
forensic implications of image laundering, revealing a serious potential to
obscure traces of real content, including sensitive and harmful materials that
could be mistakenly classified as synthetic, thereby undermining the protection
of individuals depicted. To address this issue, we propose a two-stage
detection pipeline that effectively differentiates between pristine, laundered,
and fully synthetic images (those generated from text prompts), showing
robustness across various conditions. Finally, we highlight another alarming
property of image laundering, which appears to mask the unique artifacts
exploited by forensic detectors to solve the camera model identification task,
strongly undermining their performance. Our experimental code is available at
https://github.com/polimi-ispl/synthetic-image-detection.
