Live GPU Forensics: The Process of Recovering Video Frames from NVIDIA GPU

The purpose of this research is to apply a graphics processing unit (GPU) forensics method to recover video artifacts from NVIDIA GPU. The tested video specs are 512 x 512 in resolution for video 1 and 800 x 600 in resolution for video 2. Both videos are mpeg4 video codec. A VLC player was used in the experiment. A special program has been developed using OpenCL to recover 1) patterns that are frames consist of pixel values and 2) dump data from the GPU global memory. The dump data that represent the video frame were located using simple steps. The recovery process was successful. For 512 x 512 resolution video, the frames were partially recovered but it shows enough information for the forensics investigator to determine what was viewed last. The research indicates that it is harder, but not impossible, to obtain a viewable frame from higher-resolution vide

Data remanence and digital forensic investigation for CUDA Graphics Processing Units

This paper investigates the practicality of memory attacks on commercial Graphics Processing Units (GPUs). With recent advances in the performance and viability of using GPUs for various highly-parallelised data processing tasks, a number of security challenges are raised. Unscrupulous software running subsequently on the same GPU, either by the same user, or another user, in a multi-user system, may be able to gain access to the contents of the GPU memory. This contains data from previous program executions. In certain use-cases, where the GPU is used to offload intensive parallel processing such as pattern matching for an intrusion detection system, financial systems, or cryptographic algorithms, it may be possible for the GPU memory to contain privileged data, which would ordinarily be inaccessible to an unprivileged application running on the host computer. With GPUs potentially yielding access to confidential information, existing research in the field is built upon, to investigate the practicality of extracting data from global, shared and texture memory, and retrieving this data for further analysis. These techniques are also implemented on various GPUs using three different Nvidia CUDA versions. A novel methodology for digital forensic examination of GPU memory for remanent data is then proposed, along with some suggestions and considerations towards countermeasures and anti-forensic technique