Blind Image Watermarking using Normalized STDM robust against Fixed Gain Attack

Hatoum, Makram; Darazi, Rony; Couchot, Jean-François

doi:10.1109/imcet.2018.8603038

Cited by 4 publications

(4 citation statements)

References 19 publications

(20 reference statements)

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“…Li and Cox [34] improved the robustness of STDM against amplitude scaling and JPEG compression using a perceptual model based on Watson's model. Hatoum et al [35] proposed a blind image watermarking using Normalized STDM robust against FGA attack, AWGN attack, and JPEG compression. Recently, CNN was exploited for modeling images priors for denoising.…”

Section: Related Workmentioning

confidence: 99%

Using Deep learning for image watermarking attack

Hatoum¹,

Couchot²,

Couturier³

et al. 2021

Signal Processing: Image Communication

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Digital image watermarking has justified its suitability for copyright protection and copy control of digital images. In the past years, various watermarking schemes were proposed to enhance the fidelity and the robustness of watermarked images against different types of attacks such as additive noise, filtering, and geometric attacks. It is highly important to guarantee a sufficient level of robustness of watermarked images against such type of attacks. Recently, Deep learning and neural networks achieved noticeable development and improvement, especially in image processing, segmentation, and classification. Therefore, in this paper, we studied the effect of a Fully Convolutional Neural Network (FCNN), as a denoising attack, on watermarked images. This deep architecture improves the training process and denoising performance, through which the encoder-decoder remove the noise while preserving the detailed structure of the image. FCNNDA outperforms the other types of attacks because it destroys the watermarks while preserving a good quality of the attacked images. Spread Transform Dither Modulation (STDM) and Spread Spectrum (SS) are used as watermarking schemes to embed the watermarks in the images using several scenarios. This evaluation shows that such type of denoising attack preserves the image quality while breaking the robustness of all evaluated watermarked schemes. It could also be considered a deleterious attack.

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Section: Related Workmentioning

confidence: 99%

Using Deep learning for image watermarking attack

Hatoum¹,

Couchot²,

Couturier³

et al. 2021

Signal Processing: Image Communication

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“…Frequency domain watermarking first involves transforming the host image, commonly including matrix decomposition, three major transformations (namely discrete wavelet transform (DWT) [ 21 , 22 , 23 , 24 , 25 ], discrete cosine transform (DCT) [ 26 , 27 , 28 , 29 , 30 ], and discrete Fourier transform (DFT) [ 31 , 32 , 33 , 34 ]), and other transformations [ 20 , 35 , 36 , 37 , 38 , 39 ], followed by modifying the transform coefficients to embed the watermark.…”

Section: Introductionmentioning

confidence: 99%

“…The watermark is extracted by comparing the levels of the detection matrix generated from the received image using a key, achieving a very high embedding capacity. The scheme proposed by Hatoum et al [ 30 ] adopts spread transform dithering modulation (STDM), which applies DCT to the host image when embedding the watermark, and then embeds the watermark bit through NSTDM (normalized STDM). After applying IDCT in the extraction stage, the NSTDM decoder is implemented to detect the watermark bit.…”

Section: Introductionmentioning

confidence: 99%

Covert Communication through Robust Fragment Hiding in a Large Number of Images

Wang,

Zhong,

Feng

et al. 2024

Sensors

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For covert communication in lossy channels, it is necessary to consider that the carrier of the hidden watermark will undergo multiple image-processing attacks. In order to ensure that secret information can be extracted without distortion from the watermarked images that have undergone attacks, in this paper, we design a novel fragmented secure communication system. The sender will fragment the secret data to be transmitted and redundantly hide it in a large number of multimodal carriers of messenger accounts on multiple social platforms. The receiver receives enough covert carriers, extracts each fragment, and concatenates the transmitted secret data. This article uses the image carrier as an example to fragment the text file intended for transmission and embeds it into a large number of images, with each fragment being redundant and embedded into multiple images. In this way, at the receiving end, only enough stego images need to be received to extract the information in each image, and then concatenate the final secret file. In order to resist various possible attacks during image transmission, we propose a strong robust image watermarking method. This method adopts a watermark layer based on DFT, which has high embedding and detection efficiency and good invisibility. Secondly, a watermark layer based on DCT is adopted, which can resist translation attacks, JPEG attacks, and other common attacks. Experiments have shown that our watermarking method is very fast; both the embedding time and the extraction time are less than 0.15 s for images not larger than 2000×2000. Our watermarking method has very good invisibility with 41dB PSNR on average. And our watermarking method is more robust than existing schemes and robust to nearly all kinds of attacks. Based on this strong robust image watermarking method, the scheme of fragmenting and hiding redundant transmission content into a large number of images is effective and practical. Our scheme can 100% restore the secret file completely under different RST or hybrid attacks, such as rotation by 1 degree and 5 degrees, scaling by 1.25 and 0.8, and cropping by 10% and 25%. Our scheme can successfully restore the secret file completely even if 30% of received images are lost. When 80% of received images are lost, our scheme can still restore 61.1% of the secret file. If all stego images can be obtained, the original text file can be completely restored.

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“…Spread Transform Dither Modulation (STDM), a special case of Quantization Index Modulation (QIM), has been widely used for digital image watermarking, and in previous work we have applied the STDM watermarking scheme in PDF documents for copyright protection with a tradeoff between the transparency and robustness [4]. STDM achieves high robustness against additive noise attack, but it is largely vulnerable to the FGA attack [16]. In this type of attack, the received signal is indeed multiplied by a gain factor ρ, which scales the watermark vector and shifts it away from its original quantization cell.…”

Section: Introductionmentioning

confidence: 99%

Normalized blind STDM watermarking scheme for images and PDF documents robust against fixed gain attack

Hatoum

Darazi

Couchot

2019

Multimed Tools Appl

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Spread Transform Dither Modulation (STDM), a special case of Quantization Index Modulation (QIM), has been widely used in digital watermarking. STDM has good performance in robustness against re-quantization and random noise attacks, but it is largely vulnerable to the Fixed Gain Attack (FGA). In addition to digital images and videos watermarking applications, copyright protection for digital text such as Portable Document Format (PDF) has received particular attention and interest. In this paper, we modify the STDM watermarking scheme by making the quantization step dependent on the original samples during the embedding process and on the watermarked samples during the decoding process to resist the FGA attack and enhance the robustness against the Additive White Gaussian Noise (AWGN) attack and JPEG compression attack in both the spatial domain and frequency domain regardless of the source of elements used as cover work. Experimentations have been conducted distinctly on digital images and text PDF documents. The tested images were watermarked with a uniform fidelity, where SSIM is fixed to 0.982 and 0.953. Our approach achieves significant robustness against the FGA attack with an improvement of 98% in terms of Bit Error Rates (BER) compared to traditional STDM. As for the AWGN attack, an improvement of 21% is shown. The proposed method also presents robustness against a variety of filtering and geometric attacks, while preserving a high level of transparency.

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Blind Image Watermarking using Normalized STDM robust against Fixed Gain Attack

Cited by 4 publications

References 19 publications

Using Deep learning for image watermarking attack

Using Deep learning for image watermarking attack

Covert Communication through Robust Fragment Hiding in a Large Number of Images

Normalized blind STDM watermarking scheme for images and PDF documents robust against fixed gain attack

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