Abstract: A framework is provided for reducing the number of locations modified when hiding data, such as a digital watermark, in binary data. The framework complements data hiding techniques, such as digital watermarking techniques. After determining potential embedding locations according to an underlying technique, a data structure is created with values associated with those locations. A parity calculation is performed on the values in the data structure. The calculated parity is compared with hidden data to determine locations for modifications. Manipulations are then performed to reduce the total number of modifications needed to represent the hidden data. Modifications are made to the binary data according to the underlying technique. During decoding of the hidden data, the same locations can be determined, the same data structure can be created with the modified values, and a parity calculation is then performed to decode the hidden data.

Abstract: System and methodologies are provided herein for low-distortion image watermarking. Parity relationships between multiple pixels and/or regions of an image can be leveraged as described herein to reduce the amount of toggling required to embed a watermark in an image, thereby increasing watermark efficiency and reducing visual artifacts introduced in a watermarked image. Various algorithms for leveraging parity relationships are described herein, including a Block-Overlapping Parity Check (BOPC) algorithm, a Champagne Pyramid Parity Check (CPPC) algorithm, and a Tree-Based Parity Check (TBPC) algorithm.

Abstract: A framework is provided for reducing the number of locations modified when hiding data, such as a digital watermark, in binary data. The framework complements data hiding techniques, such as digital watermarking techniques. After determining potential embedding locations according to an underlying technique, a data structure is created with values associated with those locations. A parity calculation is performed on the values in the data structure. The calculated parity is compared with hidden data to determine locations for modifications. Manipulations are then performed to reduce the total number of modifications needed to represent the hidden data. Modifications are made to the binary data according to the underlying technique. During decoding of the hidden data, the same locations can be determined, the same data structure can be created with the modified values, and a parity calculation is then performed to decode the hidden data.

Abstract: Systems and methodologies are provided herein for representing information in a data processing system with low power consumption. As described herein, parity relationships between multiple nodes of to-be-written binary information and original information can be leveraged as described herein to reduce the amount of toggling required to write information in a memory, thereby reducing power consumption. Various algorithms for leveraging parity relationships are described herein, including a Champagne Pyramid Parity Check (CPPC) algorithm and a Tree-Based Parity Check (TBPC) algorithm.

Abstract: System and methodologies are provided herein for low-distortion image watermarking. Parity relationships between multiple pixels and/or regions of an image can be leveraged as described herein to reduce the amount of toggling required to embed a watermark in an image, thereby increasing watermark efficiency and reducing visual artifacts introduced in a watermarked image. Various algorithms for leveraging parity relationships are described herein, including a Block-Overlapping Parity Check (BOPC) algorithm, a Champagne Pyramid Parity Check (CPPC) algorithm, and a Tree-Based Parity Check (TBPC) algorithm.