408 research outputs found
Blind and robust images watermarking based on wavelet and edge insertion
This paper gives a new scheme of watermarking technique related to insert the
mark by adding edge in HH sub-band of the host image after wavelet
decomposition. Contrary to most of the watermarking algorithms in wavelet
domain, our method is blind and results show that it is robust against the JPEG
and GIF compression, histogram and spectrum spreading, noise adding and small
rotation. Its robustness against compression is better than others watermarking
algorithms reported in the literature. The algorithm is flexible because its
capacity or robustness can be improved by modifying some parameters.Comment: 8 page
A Non-Blind Watermarking Scheme for Gray Scale Images in Discrete Wavelet Transform Domain using Two Subbands
Digital watermarking is the process to hide digital pattern directly into a
digital content. Digital watermarking techniques are used to address digital
rights management, protect information and conceal secrets. An invisible
non-blind watermarking approach for gray scale images is proposed in this
paper. The host image is decomposed into 3-levels using Discrete Wavelet
Transform. Based on the parent-child relationship between the wavelet
coefficients the Set Partitioning in Hierarchical Trees (SPIHT) compression
algorithm is performed on the LH3, LH2, HL3 and HL2 subbands to find out the
significant coefficients. The most significant coefficients of LH2 and HL2
bands are selected to embed a binary watermark image. The selected significant
coefficients are modulated using Noise Visibility Function, which is considered
as the best strength to ensure better imperceptibility. The approach is tested
against various image processing attacks such as addition of noise, filtering,
cropping, JPEG compression, histogram equalization and contrast adjustment. The
experimental results reveal the high effectiveness of the method.Comment: 9 pages, 7 figure
HiDDeN: Hiding Data With Deep Networks
Recent work has shown that deep neural networks are highly sensitive to tiny
perturbations of input images, giving rise to adversarial examples. Though this
property is usually considered a weakness of learned models, we explore whether
it can be beneficial. We find that neural networks can learn to use invisible
perturbations to encode a rich amount of useful information. In fact, one can
exploit this capability for the task of data hiding. We jointly train encoder
and decoder networks, where given an input message and cover image, the encoder
produces a visually indistinguishable encoded image, from which the decoder can
recover the original message. We show that these encodings are competitive with
existing data hiding algorithms, and further that they can be made robust to
noise: our models learn to reconstruct hidden information in an encoded image
despite the presence of Gaussian blurring, pixel-wise dropout, cropping, and
JPEG compression. Even though JPEG is non-differentiable, we show that a robust
model can be trained using differentiable approximations. Finally, we
demonstrate that adversarial training improves the visual quality of encoded
images
Copyright Protection of Color Imaging Using Robust-Encoded Watermarking
In this paper we present a robust-encoded watermarking method applied to color images for copyright protection, which presents robustness against several geometric and signal processing distortions. Trade-off between payload, robustness and imperceptibility is a very important aspect which has to be considered when a watermark algorithm is designed. In our proposed scheme, previously to be embedded into the image, the watermark signal is encoded using a convolutional encoder, which can perform forward error correction achieving better robustness performance. Then, the embedding process is carried out through the discrete cosine transform domain (DCT) of an image using the image normalization technique to accomplish robustness against geometric and signal processing distortions. The embedded watermark coded bits are extracted and decoded using the Viterbi algorithm. In order to determine the presence or absence of the watermark into the image we compute the bit error rate (BER) between the recovered and the original watermark data sequence. The quality of the watermarked image is measured using the well-known indices: Peak Signal to Noise Ratio (PSNR), Visual Information Fidelity (VIF) and Structural Similarity Index (SSIM). The color difference between the watermarked and original images is obtained by using the Normalized Color Difference (NCD) measure. The experimental results show that the proposed method provides good performance in terms of imperceptibility and robustness. The comparison among the proposed and previously reported methods based on different techniques is also provided
Secured color image watermarking technique in DWT-DCT domain
The multilayer secured DWT-DCT and YIQ color space based image watermarking
technique with robustness and better correlation is presented here. The
security levels are increased by using multiple pn sequences, Arnold
scrambling, DWT domain, DCT domain and color space conversions. Peak signal to
noise ratio and Normalized correlations are used as measurement metrics. The
512x512 sized color images with different histograms are used for testing and
watermark of size 64x64 is embedded in HL region of DWT and 4x4 DCT is used.
'Haar' wavelet is used for decomposition and direct flexing factor is used. We
got PSNR value is 63.9988 for flexing factor k=1 for Lena image and the maximum
NC 0.9781 for flexing factor k=4 in Q color space. The comparative performance
in Y, I and Q color space is presented. The technique is robust for different
attacks like scaling, compression, rotation etc.Comment: 9 pages; International Journal of Computer Science, Engineering and
Information Technology (IJCSEIT), Vol.1, No. 3, August 201
High Resilience Diverse Domain Multilevel Audio Watermarking with Adaptive Threshold
A novel diverse domain (DCT-SVD & DWT-SVD) watermarking scheme is proposed in
this paper. Here, the watermark is embedded simultaneously onto the two
domains. It is shown that an audio signal watermarked using this scheme has
better subjective and objective quality when compared with other watermarking
schemes. Also proposed are two novel watermark detection algorithms viz., AOT
(Adaptively Optimised Threshold) and AOTx (AOT eXtended). The fundamental idea
behind both is finding an optimum threshold for detecting a known character
embedded along with the actual watermarks in a known location, with the
constraint that the Bit Error Rate (BER) is minimum. This optimum threshold is
used for detecting the other characters in the watermarks. This approach is
shown to make the watermarking scheme less susceptible to various signal
processing attacks, thus making the watermarks more robust
Entropy Based Robust Watermarking Algorithm
Tänu aina kasvavale multimeedia andmeedastus mahtudele Internetis, on esile kerkinud mured turvalisusest ja piraatlusest. Digitaalse meedia paljundamise ja muutmise maht on loonud vajaduse digitaalse meedia vesimärgistamise järgi. Selles töös on tutvustatud vastupidavaid vesimärkide lisamise algoritme, mis lisavad vesimärgid madala entroopiaga pildi osadesse. Välja pakutud algoritmides jagatakse algne pilt blokkidesse ning arvutatakse iga bloki entroopia. Kõikide blokkide keskmine entroopia väärtus valitakse künniseks, mille järgi otsustatakse, millistesse blokkidesse vesimärk lisada. Kõik blokid, mille entroopia on väiksem kui künnis, viiakse signaali sageduse kujule kasutades Discrete Wavelet Transform algoritmi. Madala sagedusega sagedusvahemikule rakendatakse Chirp Z-Transform algoritmi ja saadud tulemusele LU-dekompositsiooni või QR-dekompositsiooni. Singular Value Decomposition meetodi rakendamisel diagonaalmaatriksile, mis saadi eelmisest sammust, saadakse iga bloki vastav väärtus. Vesimärk lisatakse pildile, liites iga bloki arvutatud väärtusele vesimärgi Singular Value Decomposition meetodi tulemused. Kirjeldatud algoritme testiti ning võrreldi teiste tavapärast ning uudsete vesimärkide lisamise tehnoloogiatega. Kvantitatiivsed ja kvalitatiivsed eksperimendid näitavad, et välja pakutud meetodid on tajumatud ning vastupidavad signaali töötlemise rünnakutele.With growth of digital media distributed over the Internet, concerns about security and piracy have emerged. The amount of digital media reproduction and tampering has brought a need for content watermarking. In this work, multiple robust watermarking algorithms are introduced. They embed watermark image into singular values of host image’s blocks with low entropy values. In proposed algorithms, host image is divided into blocks, and the entropy of each block is calculated. The average of all entropies indicates the chosen threshold value for selecting the blocks in which watermark image should be embedded. All blocks with entropy lower than the calculated threshold are decomposed into frequency subbands using discrete wavelet transform (DWT). Subsequently chirp z-transform (CZT) is applied to the low-frequency subband followed by an appropriate matrix decomposition such as lower and upper decomposition (LUD) or orthogonal-triangular decomposition (QR decomposition). By applying singular value decomposition (SVD) to diagonal matrices obtained by the aforementioned matrix decompositions, the singular values of each block are calculated. Watermark image is embedded by adding singular values of the watermark image to singular values of the low entropy blocks. Proposed algorithms are tested on many host and watermark images, and they are compared with conventional and other state-of-the-art watermarking techniques. The quantitative and qualitative experimental results are indicating that the proposed algorithms are imperceptible and robust against many signal processing attacks
Fraternal Twins: Unifying Attacks on Machine Learning and Digital Watermarking
Machine learning is increasingly used in security-critical applications, such
as autonomous driving, face recognition and malware detection. Most learning
methods, however, have not been designed with security in mind and thus are
vulnerable to different types of attacks. This problem has motivated the
research field of adversarial machine learning that is concerned with attacking
and defending learning methods. Concurrently, a different line of research has
tackled a very similar problem: In digital watermarking information are
embedded in a signal in the presence of an adversary. As a consequence, this
research field has also extensively studied techniques for attacking and
defending watermarking methods.
The two research communities have worked in parallel so far, unnoticeably
developing similar attack and defense strategies. This paper is a first effort
to bring these communities together. To this end, we present a unified notation
of black-box attacks against machine learning and watermarking that reveals the
similarity of both settings. To demonstrate the efficacy of this unified view,
we apply concepts from watermarking to machine learning and vice versa. We show
that countermeasures from watermarking can mitigate recent model-extraction
attacks and, similarly, that techniques for hardening machine learning can fend
off oracle attacks against watermarks. Our work provides a conceptual link
between two research fields and thereby opens novel directions for improving
the security of both, machine learning and digital watermarking
A collusion attack on digital video watermarks based on the replacement strategy
Digital works such as images, audio and video present security concerns due to their portability and error free reproducibility. Thus, digital work producers are not being properly compensated for copyrighted works that are illegally copied and distributed on the Internet. One solution that has been proposed to solve some of these problems is digital watermarking. Researchers have proposed many different watermarking methods, but for any of these methods to be commercially applicable, they must be secure in the sense of being resilient to all known watermarking attacks. Therefore, the exploration and examination of watermarking attacks must be exhaustive. This paper adds to the knowledge base of known watermarking attacks on digital video. Specifically a type of collusion attack based on the replacement attack strategy is applied and tested against two digital video watermarking schemes. The effectiveness of this attack is measured by evaluating the fidelity of the attacked video as well as the ability of the attack to remove the watermark. This attack will provide yet another quality standard for measuring the effectiveness of watermarking schemes. This standard must be met if watermarking is to be a commercially viable option
A New Digital Watermarking Algorithm Using Combination of Least Significant Bit (LSB) and Inverse Bit
In this paper, we introduce a new digital watermarking algorithm using least
significant bit (LSB). LSB is used because of its little effect on the image.
This new algorithm is using LSB by inversing the binary values of the watermark
text and shifting the watermark according to the odd or even number of pixel
coordinates of image before embedding the watermark. The proposed algorithm is
flexible depending on the length of the watermark text. If the length of the
watermark text is more than ((MxN)/8)-2 the proposed algorithm will also embed
the extra of the watermark text in the second LSB. We compare our proposed
algorithm with the 1-LSB algorithm and Lee's algorithm using Peak
signal-to-noise ratio (PSNR). This new algorithm improved its quality of the
watermarked image. We also attack the watermarked image by using cropping and
adding noise and we got good results as well.Comment: 8 pages, 6 figures and 4 tables; Journal of Computing, Volume 3,
Issue 4, April 2011, ISSN 2151-961
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