337 research outputs found
Scene-based imperceptible-visible watermarking for HDR video content
This paper presents the High Dynamic Range - Imperceptible Visible Watermarking for HDR video content (HDR-IVW-V) based on scene detection for robust copyright protection of HDR videos using a visually imperceptible watermarking methodology. HDR-IVW-V employs scene detection to reduce both computational complexity and undesired visual attention to watermarked regions. Visual imperceptibility is achieved by finding the region of a frame with the highest hiding capacities on which the Human Visual System (HVS) cannot recognize the embedded watermark. The embedded watermark remains visually imperceptible as long as the normal color calibration parameters are held. HDR-IVW-V is evaluated on PQ-encoded HDR video content successfully attaining visual imperceptibility, robustness to tone mapping operations and image quality preservation
Additional information delivery to image content via improved unseen–visible watermarking
In a practical watermark scenario, watermarks are used to provide auxiliary information; in this way, an analogous digital approach called unseen–visible watermark has been introduced to deliver auxiliary information. In this algorithm, the embedding stage takes advantage of the visible and invisible watermarking to embed an owner logotype or barcodes as watermarks; in the exhibition stage, the equipped functions of the display devices are used to reveal the watermark to the naked eyes, eliminating any watermark exhibition algorithm. In this paper, a watermark complement strategy for unseen–visible watermarking is proposed to improve the embedding stage, reducing the histogram distortion and the visual degradation of the watermarked image. The presented algorithm exhibits the following contributions: first, the algorithm can be applied to any class of images with large smooth regions of low or high intensity; second, a watermark complement strategy is introduced to reduce the visual degradation and histogram distortion of the watermarked image; and third, an embedding error measurement is proposed. Evaluation results show that the proposed strategy has high performance in comparison with other algorithms, providing a high visual quality of the exhibited watermark and preserving its robustness in terms of readability and imperceptibility against geometric and processing attacks
Preventing Unauthorized AI Over-Analysis by Medical Image Adversarial Watermarking
The advancement of deep learning has facilitated the integration of
Artificial Intelligence (AI) into clinical practices, particularly in
computer-aided diagnosis. Given the pivotal role of medical images in various
diagnostic procedures, it becomes imperative to ensure the responsible and
secure utilization of AI techniques. However, the unauthorized utilization of
AI for image analysis raises significant concerns regarding patient privacy and
potential infringement on the proprietary rights of data custodians.
Consequently, the development of pragmatic and cost-effective strategies that
safeguard patient privacy and uphold medical image copyrights emerges as a
critical necessity. In direct response to this pressing demand, we present a
pioneering solution named Medical Image Adversarial watermarking (MIAD-MARK).
Our approach introduces watermarks that strategically mislead unauthorized AI
diagnostic models, inducing erroneous predictions without compromising the
integrity of the visual content. Importantly, our method integrates an
authorization protocol tailored for legitimate users, enabling the removal of
the MIAD-MARK through encryption-generated keys. Through extensive experiments,
we validate the efficacy of MIAD-MARK across three prominent medical image
datasets. The empirical outcomes demonstrate the substantial impact of our
approach, notably reducing the accuracy of standard AI diagnostic models to a
mere 8.57% under white box conditions and 45.83% in the more challenging black
box scenario. Additionally, our solution effectively mitigates unauthorized
exploitation of medical images even in the presence of sophisticated watermark
removal networks. Notably, those AI diagnosis networks exhibit a meager average
accuracy of 38.59% when applied to images protected by MIAD-MARK, underscoring
the robustness of our safeguarding mechanism
WM-NET: Robust Deep 3D Watermarking with Limited Data
The goal of 3D mesh watermarking is to embed the message in 3D meshes that
can withstand various attacks imperceptibly and reconstruct the message
accurately from watermarked meshes. Traditional methods are less robust against
attacks. Recent DNN-based methods either introduce excessive distortions or
fail to embed the watermark without the help of texture information. However,
embedding the watermark in textures is insecure because replacing the texture
image can completely remove the watermark. In this paper, we propose a robust
deep 3D mesh watermarking WM-NET, which leverages attention-based convolutions
in watermarking tasks to embed binary messages in vertex distributions without
texture assistance. Furthermore, our WM-NET exploits the property that
simplified meshes inherit similar relations from the original ones, where the
relation is the offset vector directed from one vertex to its neighbor. By
doing so, our method can be trained on simplified meshes(limited data) but
remains effective on large-sized meshes (size adaptable) and unseen categories
of meshes (geometry adaptable). Extensive experiments demonstrate our method
brings 50% fewer distortions and 10% higher bit accuracy compared to previous
work. Our watermark WM-NET is robust against various mesh attacks, e.g. Gauss,
rotation, translation, scaling, and cropping
Traceable and Authenticable Image Tagging for Fake News Detection
To prevent fake news images from misleading the public, it is desirable not
only to verify the authenticity of news images but also to trace the source of
fake news, so as to provide a complete forensic chain for reliable fake news
detection. To simultaneously achieve the goals of authenticity verification and
source tracing, we propose a traceable and authenticable image tagging approach
that is based on a design of Decoupled Invertible Neural Network (DINN). The
designed DINN can simultaneously embed the dual-tags, \textit{i.e.},
authenticable tag and traceable tag, into each news image before publishing,
and then separately extract them for authenticity verification and source
tracing. Moreover, to improve the accuracy of dual-tags extraction, we design a
parallel Feature Aware Projection Model (FAPM) to help the DINN preserve
essential tag information. In addition, we define a Distance Metric-Guided
Module (DMGM) that learns asymmetric one-class representations to enable the
dual-tags to achieve different robustness performances under malicious
manipulations. Extensive experiments, on diverse datasets and unseen
manipulations, demonstrate that the proposed tagging approach achieves
excellent performance in the aspects of both authenticity verification and
source tracing for reliable fake news detection and outperforms the prior
works
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