Abstract: As image source attribution techniques have become significantly sophisticated and are now becoming commonplace, there is a growing need for capabilities to anonymize images and videos. Focusing on the photo response non-uniformity noise pattern based sensor fingerprinting technique, this work evaluates the effectiveness of well-established seam carving method to defend against sensor fingerprint matching. We consider ways in which seam-carving based anonymization can be countered and propose enhancements over conventional seam carving method. Our results show that applying geometrical distortion in addition to seam carving will make counter attack very ineffective both in terms of computational complexity and accuracy.

Abstract: A media signal is accessed, which has been generated with one or more first processing operations. The media signal includes one or more sets of artifacts, which respectively result from the one or more processing operations. One or more features are extracted from the accessed media signal. The extracted features each respectively correspond to the one or more artifact sets. Based on the extracted features, a conditional probability score and/or a heuristically based score is computed, which relates to the one or more first processing operations.

Abstract: As image source attribution techniques have become significantly sophisticated and are now becoming commonplace, there is a growing need for capabilities to anonymize images and videos. Focusing on the photo response non-uniformity noise pattern based sensor fingerprinting technique, this work evaluates the effectiveness of well-established seam carving method to defend against sensor fingerprint matching. We consider ways in which seam-carving based anonymization can be countered and propose enhancements over conventional seam carving method. Our results show that applying geometrical distortion in addition to seam carving will make counter attack very ineffective both in terms of computational complexity and accuracy.

Abstract: For user authentication, finger touch information from a user is accepted via a touch sensitive element, the finger touch information including at least a time series of finger touch samples that define a trace of the user's signature, and each of the finger touch samples including centroid coordinates and non-centroid information, the non-centroid information including at least one of (A) a shape of the finger touch sample, (B) a size of the finger touch sample, (C) an orientation of the finger touch sample, and (D) characteristics of a multi-touch finger touch sample. A similarity of such finger touch samples with previously entered and stored finger touch samples is determined and compared with a threshold for purposes of user authentication.

Abstract: A media signal is accessed, which has been generated with one or more first processing operations. The media signal includes one or more sets of artifacts, which respectively result from the one or more processing operations. One or more features are extracted from the accessed media signal. The extracted features each respectively correspond to the one or more artifact sets. Based on the extracted features, a conditional probability score and/or a heuristically based score is computed, which relates to the one or more first processing operations.

Abstract: Several promising techniques have been recently proposed to bind an image or video to its source acquisition device. These techniques have been intensively studied to address performance issues, but the computational efficiency aspect has not been given due consideration. Considering very large databases, the efficiency of the sensor fingerprint based source device identification technique is described. Novel schemes that improve search complexity as compared to a conventional approach are described.

Abstract: For user authentication, finger touch information from a user is accepted via a touch sensitive element, the finger touch information including at least a time series of finger touch samples that define a trace of the user's signature, and each of the finger touch samples including centroid coordinates and non-centroid information, the non-centroid information including at least one of (A) a shape of the finger touch sample, (B) a size of the finger touch sample, (C) an orientation of the finger touch sample, and (D) characteristics of a multi-touch finger touch sample. A similarity of such finger touch samples with previously entered and stored finger touch samples is determined and compared with a threshold for purposes of user authentication.