Least significant bit

About: Least significant bit is a research topic. Over the lifetime, 4915 publications have been published within this topic receiving 59226 citations. The topic is also known as: LSB & right-most bit.

A digital watermark

Abstract: The paper discusses the feasibility of coding an "undetectable" digital water mark on a standard 512/spl times/512 intensity image with an 8 bit gray scale. The watermark is capable of carrying such information as authentication or authorisation codes, or a legend essential for image interpretation. This capability is envisaged to find application in image tagging, copyright enforcement, counterfeit protection, and controlled access. Two methods of implementation are discussed. The first is based on bit plane manipulation of the LSB, which offers easy and rapid decoding. The second method utilises linear addition of the water mark to the image data, and is more difficult to decode, offering inherent security. This linearity property also allows some image processing, such as averaging, to take place on the image, without corrupting the water mark beyond recovery. Either method is potentially compatible with JPEG and MPEG processing. >

Reversible data hiding

Abstract: We present a novel reversible (lossless) data hiding (embedding) technique, which enables the exact recovery of the original host signal upon extraction of the embedded information. A generalization of the well-known LSB (least significant bit) modification is proposed as the data embedding method, which introduces additional operating points on the capacity-distortion curve. Lossless recovery of the original is achieved by compressing portions of the signal that are susceptible to embedding distortion, and transmitting these compressed descriptions as a part of the embedded payload. A prediction-based conditional entropy coder which utilizes static portions of the host as side-information improves the compression efficiency, and thus the lossless data embedding capacity.

Lossless generalized-LSB data embedding

Abstract: We present a novel lossless (reversible) data-embedding technique, which enables the exact recovery of the original host signal upon extraction of the embedded information. A generalization of the well-known least significant bit (LSB) modification is proposed as the data-embedding method, which introduces additional operating points on the capacity-distortion curve. Lossless recovery of the original is achieved by compressing portions of the signal that are susceptible to embedding distortion and transmitting these compressed descriptions as a part of the embedded payload. A prediction-based conditional entropy coder which utilizes unaltered portions of the host signal as side-information improves the compression efficiency and, thus, the lossless data-embedding capacity.

A 1.5-V, 10-bit, 14.3-MS/s CMOS pipeline analog-to-digital converter

Abstract: A 1.5-V, 10-bit, 14.3-MS/s pipeline analog-to-digital converter was implemented in a 0.6 /spl mu/m CMOS technology. Emphasis was placed on observing device reliability constraints at low voltage. MOS switches were implemented without low-threshold devices by using a bootstrapping technique that does not subject the devices to large terminal voltages. The converter achieved a peak signal-to-noise-and-distortion ratio of 58.5 dB, maximum differential nonlinearity of 11.5 least significant bit (LSB), maximum integral nonlinearity of 0.7 LSB, and a power consumption of 36 mW.

Steganalysis by Subtractive Pixel Adjacency Matrix

Abstract: This paper presents a method for detection of steganographic methods that embed in the spatial domain by adding a low-amplitude independent stego signal, an example of which is least significant bit (LSB) matching. First, arguments are provided for modeling the differences between adjacent pixels using first-order and second-order Markov chains. Subsets of sample transition probability matrices are then used as features for a steganalyzer implemented by support vector machines. The major part of experiments, performed on four diverse image databases, focuses on evaluation of detection of LSB matching. The comparison to prior art reveals that the presented feature set offers superior accuracy in detecting LSB matching. Even though the feature set was developed specifically for spatial domain steganalysis, by constructing steganalyzers for ten algorithms for JPEG images, it is demonstrated that the features detect steganography in the transform domain as well.