Lossless Data Embedding—New Paradigm in Digital Watermarking

By Jessica J. Fridrich, Miroslav Goljan, Rui Du. EURASIP Journal on Advances in Signal Processing, 2002

743 citations recorded by the source corpus.

One common drawback of virtually all current data embedding methods is the fact that the original image is inevitably distorted due to data embedding itself. This distortion typically cannot be removed completely due to quantization, bit-replacement, or truncation at the grayscales 0 and 255. Although the distortion is often quite small and perceptual models are used to minimize its visibility, the distortion may not be acceptable for medical imagery (for legal reasons) or for military images inspected under nonstandard viewing conditions (after enhancement or extreme zoom). In this paper, we introduce a new paradigm for data embedding in images (lossless data embedding) that has the property that the distortion due to embedding can be completely removed from the watermarked image after the embedded data has been extracted. We present lossless embedding methods for the uncompressed formats (BMP, TIFF) and for the JPEG format. We also show how the concept of lossless data embedding can be used as a powerful tool to achieve a variety of nontrivial tasks, including lossless authentication using fragile watermarks, steganalysis of LSB embedding, and distortion-free robust watermarking.

Subfields: Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/06b980931979fc9b1422b5b2bfc161d30829650e

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. Xiaolong Li
2. Bin Yang
3. Weiming Zhang
4. Dinu Coltuc
5. Y. Shi
6. Xinpeng Zhang
7. Xinlu Gui
8. Tieyong Zeng

Citations in the corpus

• Expansion Embedding Techniques for Reversible Watermarking (2007, background): “In [7] and [10], they present a novel reversible data-embedding technique by grouping the pixels and embedding data bits into the state of each group.”
• Reversible data embedding using a difference expansion (2003, background): “The motivation of reversible data embedding is distortion-free data embedding [1].”
• Improved Embedding for Prediction-Based Reversible Watermarking (2011, neutral): “The best performances reported so far [1], [2] are below 1 bit per pixel (bpp).”
• Reversible data embedding into images using wavelet techniques and sorting (2005, methodology): “Fridrich et al. [8] were among the first authors to propose techniques for reversible data embedding.”
• Reversible data hiding: Advances in the past two decades (2016, methodology): “schemes such as [18] and [21], theDEmethod performs better by providing a higher EC while keeping the distortion low.”
• Very Fast Watermarking by Reversible Contrast Mapping (2007, methodology): “M OST of the reversible watermarking approaches proposed so far incorporate a lossless data compression stage [1]–[4].”
• Lossless generalized-LSB data embedding (2005, methodology): “For our evaluation, we consider the two-pass “checkerboard” scheme that offers the highest capacity among the options evaluated in [8].”
• A Novel Reversible Data Hiding Scheme Based on Two-Dimensional Difference-Histogram Modification (2013, methodology): “the methods based on lossless compression [9]–[11], differ-”
• General Framework to Histogram-Shifting-Based Reversible Data Hiding (2013, methodology): “, the methods based on lossless compression [5]–[7],”
• DE-Based Reversible Data Hiding With Improved Overflow Location Map (2009, neutral): “[2], [6] proposed several methods to embed data.”