M. Kivanc Mihcak

Low-complexity image denoising based on statistical modeling of wavelet coefficients

We introduce a simple spatially adaptive statistical model for wavelet image coefficients and apply it to image denoising. Our model is inspired by a recent wavelet image compression algorithm, the estimation-quantization (EQ) coder. We model wavelet image coefficients as zero-mean Gaussian random variables with high local correlation. We assume a marginal prior distribution on wavelet coefficients variances and estimate them using an approximate maximum a posteriori probability rule. Then we apply an approximate minimum mean squared error estimation procedure to restore the noisy wavelet image coefficients. Despite the simplicity of our method, both in its concept and implementation, our denoising results are among the best reported in the literature.

Certifying authenticity via fiber-infused paper

A certificate of authenticity (COA) is an inexpensive physical object that has a random unique structure with high cost of near-exact reproduction. An additional requirement is that the uniqueness of COAs random structure can be verified using an inexpensive device. Bauder was the first to propose COAs created as a randomized augmentation of a set of fibers into a transparent gluing material that randomly fixes once for all the position of the fibers within. In this paper, we propose a novel system for automated verification of fiber-based COAs and outline the key challenges in enabling high cost-efficiency of such a system. The key features of the new COA scanner are simplicity, reliability, lack of any moving components, and the ability to accurately identify exact positions of individual fibers infused in COAs containing paper. The latter feature significantly increases the forging cost compared to trivial implementations of a COA scanner.

Blind image watermarking via derivation and quantization of robust semi-global statistics

We introduce a new approach for blind image watermarking: We derive robust semi-global features in wavelet domain and quantize them in order to embed the watermark. Quantization of statistics is carried out by adding scaled pseudo-random sequences that are visually unnoticeable. Our algorithm exhibits increased robustness against various attacks and it withstands standard benchmark attacks of wider trange than earlier methods (e.g., Stirmark, random bending etc.) and modifications such as compression, provided they do not cause too severe visual distortions.

Watermarking via optimization algorithms for quantizing randomized semi-global image statistics

We introduce a novel approach for blind and semi-blind watermarking and apply it to images. We derive randomized robust semi-global statistics of images in a suitable transform domain (wavelets in case of images) and quantize them in order to embed the watermark. Quantization is effectively carried out by embedding to the host a computed sequence, which is obtained by solving an optimization problem whose parameters are known to the information hider but unknown to the attacker. An essential emphasis of the proposed method is randomization, which is crucial for security and robustness against arbitrary quality-preserving attacks. We formally show that malicious optimal estimation attacks that are specifically derived for our algorithm are ineffective in practice. Furthermore, we experimentally demonstrate that our watermarking method survives many generic benchmark attacks for a large number of images.

Motion picture watermarking via quantization of pseudo-random linear statistics

In this paper, we propose a semi-blind video watermarking scheme, where we use pseudo-random robust semi-global features of video in the spatially wavelet transform domain. We design the watermark sequence via solving an optimization problem, such that the features of the mark-embedded video are the quantized versions of the features of the original video. We use a novel approach in generating the watermark such that the temporal correlation within the video frames is reflected on the generated watermark. This is achieved by i) using a special weight distribution along the time axis ii) modifying the regularization step to reflect the temporal characteristic of the host. We experimentally show the robustness of our algorithm against temporal filtering attacks and at the same time show a common weakness in 3D transform based video mark embedding methods.

Scale-invariant image watermarking via optimization algorithms for quantizing randomized statistics

We introduce a novel approach for blind and semi-blind watermarking and apply it to images. We derive randomized robust semi-global features of images in a suitable transform domain (wavelets in case of images) and quantize them in order to embed the watermark. Quantization is carried out by embedding to the host a computed sequence via solving an optimization problem whose parameters are known to the information hider, but unknown to the attacker. The image features are rationa statistics of pseudo-random regions; these statistics are by construction invariant against scaling attacks and approximately invariant against several contrast enhancement modifications (such as histogram equalization). This scheme can be seen as an improved version of our previous image watermarking algorithm [1].

An improved attack analysis on a public-key spread spectrum watermarking

We analyze the fingerprinting method in Kirovski et al. (2002) and suggest attacks that are better than the ones presented therein. To do this, we observe and use some extra information available in the system. We derive the optimal subtractive estimation attack structure in the sense of minimizing the expected value of the watermark detector statistics. Selection of different attack channel distortion constraints are also discussed. See Zhao et al. (2003) for various recently proposed attacks on spread spectrum fingerprinting systems for private watermarking.

Towards geometrically robust data-hiding with structured codebooks

In this paper we analyze performance of practical robust data-hiding in channels with geometrical transformations. By applying information-theoretic argument we show that performance of a system designed based on both random coding and random binning principles is bounded by the same maximal achievable rate for the cases when communication channel includes geometrical transformations or not. Targeting to provide theoretic performance limits of practical robust data-hiding we model it using a multiple access channel (MAC) with side information (SI) available at one of encoders and present the bounds on achievable rates of reliable communications to such a protocol. Finally, considering template-based and redundant-based design of geometrically robust data-hiding systems, we perform security analysis of their performance and present results in terms of number of trial efforts the attacker needs to completely remove hidden information.

Scalar Quadratic-Gaussian Soft Watermarking Games

We introduce a zero-sum game problem of soft watermarking: The hidden information watermark comes from a continuum and has a perceptual value; the receiver generates an estimate of the embedded watermark to minimize the expected estimation error unlike the conventional watermarking schemes where both the hidden information and the receiver output are from a discrete finite set. Applications include embedding a multimedia content into another. We study here the scalar Gaussian case and use expected mean-squared distortion. We formulate the problem as a zero-sum game between the encoder & receiver pair and the attacker. We show that for linear encoder, the optimal attacker is Gaussian-affine, derive the optimal system parameters in that case, and discuss the corresponding system behavior. We also provide numerical results to gain further insight and understanding of the system behavior at optimality.