2 citations · 3 across the 5 of their papers we have counts for
5 papers
DeepFreqMark: End-To-End Learnable Frequency-Domain Watermarking with Spherical Attack Simulation for Latent Diffusion Models
Chen-Hsiu Huang, Mario Köppen, Ja-Ling Wu
The proliferation of AI-generated images produced by Latent Diffusion Models (LDMs) has raised critical concerns regarding copyright infringement and misinformation. Although exist…
SLIC: Secure Learned Image Codec through Compressed Domain Watermarking to Defend Image Manipulation
Chen-Hsiu Huang, Ja-Ling Wu
The digital image manipulation and advancements in Generative AI, such as Deepfake, has raised significant concerns regarding the authenticity of images shared on social media. Tra…
Exploring Compressed Image Representation as a Perceptual Proxy: A Study
Chen-Hsiu Huang, Ja-Ling Wu
We propose an end-to-end learned image compression codec wherein the analysis transform is jointly trained with an object classification task. This study affirms that the compresse…
Image Data Hiding in Neural Compressed Latent Representations
Chen-Hsiu Huang, Ja-Ling Wu
We propose an end-to-end learned image data hiding framework that embeds and extracts secrets in the latent representations of a generic neural compressor. By leveraging a perceptu…
CPIPS: Learning to Preserve Perceptual Distances in End-to-End Image Compression
Chen-Hsiu Huang, Ja-Ling Wu
Lossy image coding standards such as JPEG and MPEG have successfully achieved high compression rates for human consumption of multimedia data. However, with the increasing prevalen…