a b s t r a c tHash-based carving is a technique for detecting the presence of specific "target files" on digital media by evaluating the hashes of individual data blocks, rather than the hashes of entire files. Unlike whole-file hashing, hash-based carving can identify files that are fragmented, files that are incomplete, or files that have been partially modified. Previous efforts at hash-based carving have looked for evidence of a single file or a few files. We attempt hash-based carving with a target file database of roughly a million files and discover an unexpectedly high false identification rate resulting from common data structures in Microsoft Office documents and multimedia files. We call such blocks "nonprobative blocks." We present the HASH-SETS algorithm that can determine the presence of files, and the HASH-RUNS algorithm that can reassemble files using a database of file block hashes. Both algorithms address the problem of non-probative blocks and provide results that can be used by analysts looking for target data on searched media. We demonstrate our technique using the bulk_extractor forensic tool, the hashdb hash database, and an algorithm implementation written in Python. Published by Elsevier Ltd on behalf of DFRWS. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

This publication has been developed by National Institute of Standards and Technology (NIST) to further its statutory responsibilities under the Federal Information Security Management Act (FISMA), Public Law (P.L.) 107-347. NIST is responsible for developing information security standards and guidelines, including minimum requirements for Federal information systems, but such standards and guidelines shall not apply to national security systems without the express approval of appropriate Federal officials exercising policy authority over such systems. This guideline is consistent with the requirements of the Office of Management and Budget (OMB) Circular A-130, Section 8b(3), Securing Agency Information Systems, as analyzed in Circular A 130, Appendix IV: Analysis of Key Sections. Supplemental information is provided in Circular A-130, Appendix III, Security of Federal Automated Information Resources.Nothing in this publication should be taken to contradict the standards and guidelines made mandatory and binding on Federal agencies by the Secretary of Commerce under statutory authority. Nor should these guidelines be interpreted as altering or superseding the existing authorities of the Secretary of Commerce, Director of the OMB, or any other Federal official. This publication may be used by nongovernmental organizations on a voluntary basis and is not subject to copyright in the United States. Attribution would, however, be appreciated by NIST.

No abstract

One of the key distinguishing aspects of underwater manipulation tasks is the perception challenges of the ocean environment, including turbidity, backscatter, and lighting effects. Consequently, underwater perception often relies on sonar-based measurements to estimate the vehicle’s state and surroundings, either standalone or in concert with other sensing modalities, to support the perception necessary to plan and control manipulation tasks. Simulation of the multibeam echosounder, while not a substitute for in-water testing, is a critical capability for developing manipulation strategies in the complex and variable ocean environment. Although several approaches exist in the literature to simulate synthetic sonar images, the methods in the robotics community typically use image processing and video rendering software to comply with real-time execution requirements. In addition to a lack of physics-based interaction model between sound and the scene of interest, several basic properties are absent in these rendered sonar images–notably the coherent imaging system and coherent speckle that cause distortion of the object geometry in the sonar image. To address this deficiency, we present a physics-based multibeam echosounder simulation method to capture these fundamental aspects of sonar perception. A point-based scattering model is implemented to calculate the acoustic interaction between the target and the environment. This is a simplified representation of target scattering but can produce realistic coherent image speckle and the correct point spread function. The results demonstrate that this multibeam echosounder simulator generates qualitatively realistic images with high efficiency to provide the sonar image and the physical time series signal data. This synthetic sonar data is a key enabler for developing, testing, and evaluating autonomous underwater manipulation strategies that use sonar as a component of perception.

No abstract