Biblio

Covert operations involving clandestine dealings and communication through cryptic and hidden messages have existed since time immemorial. While these do have a negative connotation, they have had their fair share of use in situations and applications beneficial to society in general. A "Dead Drop" is one such method of espionage trade craft used to physically exchange items or information between two individuals using a secret rendezvous point. With a "Dead Drop", to maintain operational security, the exchange itself is asynchronous. Information hiding in the slack space is one modern technique that has been used extensively. Slack space is the unused space within the last block allocated to a stored file. However, hiding in slack space operates under significant constraints with little resilience and fault tolerance. In this paper, we propose FROST – a novel asynchronous "Digital Dead Drop" robust to detection and data loss with tunable fault tolerance. Fault tolerance is a critical attribute of a secure and robust system design. Through extensive validation of FROST prototype implementation on Ubuntu Linux, we confirm the performance and robustness of the proposed digital dead drop to detection and data loss. We verify the recoverability of the secret message under various operating conditions ranging from block corruption and drive de-fragmentation to growing existing files on the target drive.

This paper proposes a forensic method for identifying whether an image was previously compressed by JPEG and also proposes an improved anti-forensics method to enhance the quality of noise added image. Stamm and Liu's anti-forensics method disable the detection capabilities of various forensics methods proposed in the literature, used for identifying the compressed images. However, it also degrades the quality of the image. First, we analyze the anti-forensics method and then use the decimal histogram of the coefficients to distinguish the never compressed images from the previously compressed; even the compressed image processed anti-forensically. After analyzing the noise distribution in the AF image, we propose a method to remove the Gaussian noise caused by image dithering which in turn enhances the image quality. The paper is organized in the following manner: Section I is the introduction, containing previous literature. Section II briefs Anti-forensic method proposed by Stamm et al. In section III, we have proposed a forensic approach and section IV comprises of improved anti-forensic approach. Section V covers details of experimentation followed by the conclusion.

During the last years, criminals have become aware of how digital evidences that lead them to courts and jail are collected and analyzed. Hence, they have started to develop antiforensic techniques to evade, hamper, or nullify their evidences. Nowadays, these techniques are broadly used by criminals, causing the forensic analysis to be in a state of decay. To defeat against these techniques, forensic analyst need to first identify them, and then to mitigate somehow their effects. In this paper, wereview the anti-forensic techniques and propose a new taxonomy that relates them to the initial phase of a forensic process mainly affected by each technique. Furthermore, we introduce mitigation techniques for these anti-forensic techniques, considering the chance to overcome the anti-forensic techniques and the difficulty to apply them.