Steganalysis of meshes based on 3D wavelet multiresolution analysis

Li, Zhenyu; Borş, Adrian G.

doi:10.1016/j.ins.2020.02.061

Cited by 13 publications

(5 citation statements)

References 47 publications

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“…Feng et al 19 presented a novel mesh visual quality metric that integrated saliency considerations to estimate local distortions in the mesh. Li et al 20 performed multiresolution 3D wavelet analysis, Laplacian smoothing and normalization and Wavelet Coefficient for watermarks embedding and extraction. Zhang et al 21 enhanced Reversible Data Hiding approach using prediction-error expansion and embedded the watermark in adjacent neighbors generating a ring pattern for easy prediction vertex.…”

Section: Literature Surveymentioning

confidence: 99%

Levenberg–Marquardt deep neural watermarking for 3D mesh using nearest centroid salient point learning

Narendra,

Valarmathi,

Anbarasi

et al. 2024

Sci Rep

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Watermarking is one of the crucial techniques in the domain of information security, preventing the exploitation of 3D Mesh models in the era of Internet. In 3D Mesh watermark embedding, moderately perturbing the vertices is commonly required to retain them in certain pre-arranged relationship with their neighboring vertices. This paper proposes a novel watermarking authentication method, called Nearest Centroid Discrete Gaussian and Levenberg–Marquardt (NCDG–LV), for distortion detection and recovery using salient point detection. In this method, the salient points are selected using the Nearest Centroid and Discrete Gaussian Geometric (NC–DGG) salient point detection model. Map segmentation is applied to the 3D Mesh model to segment into distinct sub regions according to the selected salient points. Finally, the watermark is embedded by employing the Multi-function Barycenter into each spatially selected and segmented region. In the extraction process, the embedded 3D Mesh image is extracted from each re-segmented region by means of Levenberg–Marquardt Deep Neural Network Watermark Extraction. In the authentication stage, watermark bits are extracted by analyzing the geometry via Levenberg–Marquardt back-propagation. Based on a performance evaluation, the proposed method exhibits high imperceptibility and tolerance against attacks, such as smoothing, cropping, translation, and rotation. The experimental results further demonstrate that the proposed method is superior in terms of salient point detection time, distortion rate, true positive rate, peak signal to noise ratio, bit error rate, and root mean square error compared to the state-of-the-art methods.

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Section: Literature Surveymentioning

confidence: 99%

Levenberg–Marquardt deep neural watermarking for 3D mesh using nearest centroid salient point learning

Narendra,

Valarmathi,

Anbarasi

et al. 2024

Sci Rep

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“…Li and Bors [122] proposed WFS228, a novel set of 228-D steganalysis features extracted using multiresolution 3D wavelet analysis [123]. The features are extracted from transformations between an input mesh and its corresponding higher and lower resolutions.…”

Section: Transformmentioning

confidence: 99%

“…High-capacity steganography [41], adaptivesteganography-based method [45], and steganography using a shifting strategy and a truncated space [47] PSB and PMN Li and Bors [122] WFS228 features extracted using multiresolution 3D wavelet analysis and fed into the FLD ensemble.…”

Section: Psbmentioning

confidence: 99%

Digital Image Steganalysis: Current Methodologies and Future Challenges

et al. 2022

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With the growing use of the internet and social media, data security has become a major issue. Thus, researchers are focusing on data security techniques such as steganography and steganalysis. Steganography is the approach of concealing the existence of secret messages in digital media for secure transmission. Steganalysis techniques aim to detect the existence of concealed messages and extract them. Digital image steganography and steganalysis techniques are classified into the spatial and transform domains. In this paper, we provide a detailed survey of the state-of-the-art works that have been performed in two-dimensional and three-dimensional image steganalysis. We present the most popular datasets and explain some steganographic methods for embedding hidden data. Steganalysis is a very difficult task due to the lack of information about the characteristics of the cover media that can be exploited to detect hidden messages. Therefore, we review studies performed on image steganalysis in the spatial and transform domains using classical machine learning and deep learning approaches. Additionally, we present open challenges and discuss some directions for future research.

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“…Li et al [13] proposed to extract edge vector features both in the Cartesian coordinate system and Laplacian coordinate system to design a 3D steganalyzer with better performance. Lately, Li and Bors [12] applied multiresolution 3D wavelet analysis to 3D steganalysis and designed a new set of 228-D steganalytic features. Inspired by the tensor voting theory, Zhou et al [34] proposed a 100-D normal voting tensor-based steganalytic feature set, which is universally acknowledged as the best steganalytic feature set [35].…”

Section: D Steganalysismentioning

confidence: 99%

Passive Defense Against 3D Adversarial Point Clouds Through the Lens of 3D Steganalysis

Zhu¹

2022

Preprint

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Nowadays, 3D data plays an indelible role in the computer vision field. However, extensive studies have proved that deep neural networks (DNNs) fed with 3D data, such as point clouds, are susceptible to adversarial examples, which aim to misguide DNNs and might bring immeasurable losses. Currently, 3D adversarial point clouds are chiefly generated in three fashions, i.e., point shifting, point adding, and point dropping. These point manipulations would modify geometrical properties and local correlations of benign point clouds more or less. Motivated by this basic fact, we propose to defend such adversarial examples with the aid of 3D steganalysis techniques. Specifically, we first introduce an adversarial attack and defense model adapted from the celebrated Prisoners' Problem in steganography to help us comprehend 3D adversarial attack and defense more generally. Then we rethink two significant but vague concepts in the field of adversarial example, namely, active defense and passive defense, from the perspective of steganalysis. Most importantly, we design a 3D adversarial point cloud detector through the lens of 3D steganalysis. Our detector is double-blind, that is to say, it does not rely on the exact knowledge of the adversarial attack means and victim models. To enable the detector to effectively detect malicious point clouds, we craft a 64-D discriminant feature set, including features related to first-order and secondorder local descriptions of point clouds. To our knowledge, this work is the first to apply 3D steganalysis to 3D adversarial example defense. Extensive experimental results demonstrate that the proposed 3D adversarial point cloud detector can achieve good detection performance on multiple types of 3D adversarial point clouds.

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Steganalysis of meshes based on 3D wavelet multiresolution analysis

Cited by 13 publications

References 47 publications

Levenberg–Marquardt deep neural watermarking for 3D mesh using nearest centroid salient point learning

Levenberg–Marquardt deep neural watermarking for 3D mesh using nearest centroid salient point learning

Digital Image Steganalysis: Current Methodologies and Future Challenges

Passive Defense Against 3D Adversarial Point Clouds Through the Lens of 3D Steganalysis

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