Abstract: Apparatus and method to verify the integrity of a digital image (i.e., deciding whether or not the entire image or just a portion has been tampered with, and/or finding the doctored area in the image). One first determines the imaging sensor's reference pattern noise, which serves as a unique fingerprint that identifies the imaging sensor that captured the image. To verify the integrity of the content in a region of the image, a correlation detector determines the presence or absence of the imaging sensor's reference pattern noise in that region, thereby verifying whether or not the image has integrity. The correlation detector can also find automatically one or more regions in the image that were tampered with.

Abstract: Apparatus and method to verify the integrity of a digital image (i.e., deciding whether or not the entire image or just a portion has been tampered with, and/or finding the doctored area in the image). One first determines the imaging sensor's reference pattern noise, which serves as a unique fingerprint that identifies the imaging sensor that captured the image. To verify the integrity of the content in a region of the image, a correlation detector determines the presence or absence of the imaging sensor's reference pattern noise in that region, thereby verifying whether or not the image has integrity. The correlation detector can also find automatically one or more regions in the image that were tampered with.

Abstract: Apparatus and method to verify the integrity of a digital image (i.e., deciding whether or not the entire image or just a portion has been tampered with, and/or finding the doctored area in the image). One first determines the imaging sensor's reference pattern noise, which serves as a unique fingerprint that identifies the imaging sensor that captured the image. To verify the integrity of the content in a region of the image, a correlation detector determines the presence or absence of the imaging sensor's reference pattern noise in that region, thereby verifying whether or not the image has integrity. The correlation detector can also find automatically one or more regions in the image that were tampered with. In another embodiment, one determines the pattern noise of only the image in question and tests that noise to determine whether or not the image has integrity.

Abstract: Current methods of embedding hidden data in an image inevitably distort the original image by noise. This distortion cannot generally be removed completely because of quantization, bit-replacement, or truncation at the grayscales 0 and 255. The distortion, though often small, may make the original image unacceptable for medical applications, or for military and law enforcement applications where an image must be inspected under unusual viewing conditions (e.g., after filtering or extreme zoom). The present invention provides high-capacity embedding of data that is lossless (or distortion-free) because, after embedded information is extracted from a cover image, we revert to an exact copy of the original image before the embedding took place. This new technique is a powerful tool for a variety of tasks, including lossless robust watermarking, lossless authentication with fragile watermarks, and steganalysis. The technique is applicable to raw, uncompressed formats (e.g., BMP, PCX, PGM, RAS, etc.

Abstract: A new technique for identifying from images a camera, or other imaging device such as a scanner, is based on the device's reference noise pattern, a unique stochastic characteristic of all common digital imaging sensors, including CCD, CMOS (Foveon™ X3), and JFET. First, one determines from images the sensor's reference pattern noise, which uniquely identifies each sensor. To identify the sensor from a given image, the presence or absence of the reference pattern noise in the image under examination is established using a correlation detector or other means.

Abstract: A new technique for identifying whether images are derived from a common imager, e.g., a camera, or other imaging device such as a scanner, based on the device's measured or inferred reference noise pattern, a unique stochastic characteristic of all common digital imaging sensors, including CCD, CMOS (Foveon™ X3), and JFET. The measured or inferred noise pattern of two images may be extracted and then cross correlated, with a high correlation being consistent with a common imager. Various preprocessing techniques may be used to improve tolerance to various types of image transform. It is also possible to perform the analysis without explicit separation of inferred image and inferred noise.

Abstract: A new technique for identifying from images a camera, or other imaging device such as a scanner, is based on the device's reference noise pattern, a unique stochastic characteristic of all common digital imaging sensors, including CCD, CMOS (Foveon™ X3), and JFET. First, one determines from images the sensor's reference pattern noise, which uniquely identifies each sensor. To identify the sensor from a given image, the presence or absence of the reference pattern noise in the image under examination is established using a correlation detector or other means.

Abstract: A new technique for identifying whether images are derived from a common imager, e.g., a camera, or other imaging device such as a scanner, based on the device's measured or inferred reference noise pattern, a unique stochastic characteristic of all common digital imaging sensors, including CCD, CMOS (Foveon™ X3), and JFET. The measured or inferred noise pattern of two images may be extracted and then cross correlated, with a high correlation being consistent with a common imager. Various preprocessing techniques may be used to improve tolerance to various types of image transform. It is also possible to perform the analysis without explicit separation of inferred image and inferred noise.

Abstract: Current methods of embedding hidden data in an image inevitably distort the original image by noise. This distortion cannot generally be removed completely because of quantization, bit-replacement, or truncation at the grayscales 0 and 255. The distortion, though often small, may make the original image unacceptable for medical applications, or for military and law enforcement applications where an image must be inspected under unusual viewing conditions (e.g., after filtering or extreme zoom). The present invention provides high-capacity embedding of data that is lossless (or distortion-free) because, after embedded information is extracted from a cover image, we revert to an exact copy of the original image before the embedding took place. This new technique is a powerful tool for a variety of tasks, including lossless robust watermarking, lossless authentication with fragile watermarks, and steganalysis. The technique is applicable to raw, uncompressed formats (e.g., BMP, PCX, PGM, RAS, etc.

Abstract: Current methods of embedding hidden data in an image inevitably distort the original image by noise. This distortion cannot generally be removed completely because of quantization, bit-replacement, or truncation at the grayscales 0 and 255. The distortion, though often small, may make the original image unacceptable for medical applications, or for military and law enforcement applications where an image must be inspected under unusual viewing conditions (e.g., after filtering or extreme zoom). The present invention provides high-capacity embedding of data that is lossless (or distortion-free) because, after embedded information is extracted from a cover image, we revert to an exact copy of the original image before the embedding took place. This new technique is a powerful tool for a variety of tasks, including lossless robust watermarking, lossless authentication with fragile watermarks, and steganalysis. The technique is applicable to raw, uncompressed formats (e.g., BMP, PCX, PGM, RAS, etc.

Abstract: Current methods of embedding hidden data in an image inevitably distort the original image by noise. This distortion cannot generally be removed completely because of quantization, bit-replacement, or truncation at the grayscales 0 and 255. The distortion, though often small, may make the original image unacceptable for medical applications, or for military and law enforcement applications where an image must be inspected under unusual viewing conditions (e.g., after filtering or extreme zoom). The present invention provides high-capacity embedding of data that is lossless (or distortion-free) because, after embedded information is extracted from a cover image, we revert to an exact copy of the original image before the embedding took place. This new technique is a powerful tool for a variety of tasks, including lossless robust watermarking, lossless authentication with fragile watermarks, and steganalysis. The technique is applicable to raw, uncompressed formats (e.g., BMP, PCX, PGM, RAS, etc.

Abstract: Current methods of embedding hidden data in an image inevitably distort the original image by noise. This distortion cannot generally be removed completely because of quantization, bit-replacement, or truncation at the grayscales 0 and 255. The distortion, though often small, may make the original image unacceptable for medical applications, or for military and law enforcement applications where an image must be inspected under unusual viewing conditions (e.g., after filtering or extreme zoom). The present invention provides high-capacity embedding of data that is lossless (or distortion-free) because, after embedded information is extracted from a cover image, we revert to an exact copy of the original image before the embedding took place. This new technique is a powerful tool for a variety of tasks, including lossless robust watermarking, lossless authentication with fragile watermarks, and steganalysis. The technique is applicable to raw, uncompressed formats (e.g., BMP, PCX, PGM, RAS, etc.

Abstract: A system and method that efficiently, accurately, and simply detect reliably least-significant-bit (“LSB”) embedding of a secret message in randomly scattered pixels. The system and method apply to both 24-bit color images and 8-bit grayscale or color images. Many commercial steganographic programs use Least Significant Bit embedding (LSB) as the method of choice to hide messages in 24-bit, 8-bit color images and in grayscale images. They do so based on the common belief that changes to the LSBs of colors cannot be detected because of noise that is always present in digital images. By inspecting the differences in capacity for lossless (invertible) embedding in the LSB and the shifted LSB plane, the present invention reliably detects messages as short as 1% of the total number of pixels (assuming 1 bit per sample). The system and method of the present invention are fast, and they provide accurate estimates for the length of the embedded secret message.

Abstract: Current methods of embedding hidden data in an image inevitably distort the original image by noise. This distortion cannot generally be removed completely because of quantization, bit-replacement, or truncation at the grayscales 0 and 255. The distortion, though often small, may make the original image unacceptable for medical applications, or for military and law enforcement applications where an image must be inspected under unusual viewing conditions (e.g., after filtering or extreme zoom). The present invention provides high-capacity embedding of data that is lossless (or distortion-free) because, after embedded information is extracted from a cover image, we revert to an exact copy of the original image before the embedding took place. This new technique is a powerful tool for a variety of tasks, including lossless robust watermarking, lossless authentication with fragile watermarks, and steganalysis. The technique is applicable to raw, uncompressed formats (e.g., BMP, PCX, PGM, RAS, etc.

Abstract: A system and method that efficiently, accurately, and simply detect reliably least-significant-bit (“LSB”) embedding of a secret message in randomly scattered pixels. The system and method apply to both 24-bit color images and 8-bit grayscale or color images. Many commercial steganographic programs use Least Significant Bit embedding (LSB) as the method of choice to hide messages in 24-bit, 8-bit color images and in grayscale images. They do so based on the common belief that changes to the LSBs of colors cannot be detected because of noise that is always present in digital images. By inspecting the differences in capacity for lossless (invertible) embedding in the LSB and the shifted LSB plane, the present invention reliably detects messages as short as 1% of the total number of pixels (assuming 1 bit per sample). The system and method of the present invention are fast, and they provide accurate estimates for the length of the embedded secret message.

Abstract: A system and method that efficiently, accurately, and simply detect reliably least-significant-bit (“LSB”) embedding of a secret message in randomly scattered pixels. The system and method apply to both 24-bit color images and 8-bit grayscale or color images. Many commercial steganographic programs use Least Significant Bit embedding (LSB) as the method of choice to hide messages in 24-bit, 8-bit color images and in grayscale images. They do so based on the common belief that changes to the LSBs of colors cannot be detected because of noise that is always present in digital images. By inspecting the differences in capacity for lossless (invertible) embedding in the LSB and the shifted LSB plane, the present invention reliably detects messages as short as 1% of the total number of pixels (assuming 1 bit per sample). The system and method of the present invention are fast, and they provide accurate estimates for the length of the embedded secret message.