Hash-Tree Anti-Tampering Schemes

Procedures that provide detection, location and correction of tampering in documents are known as anti-tampering schemes. In this paper we describe how to construct an anti-tampering scheme using a pre-computed tree of hashes. The main problems of constructing such a scheme are its computational feasibility and its candidate reduction process. We show how to solve both problems by the use of secondary hashing over a tree structure. Finally, we give brief comments on our ongoing work in this area

HIDDEN MARKOV MODELS FOR SOFTWARE PIRACY DETECTION

The unauthorized copying of software is often referred to as software piracy. Soft- ware piracy causes billions of dollars of annual losses for companies and governments worldwide. In this project, we analyze a method for detecting software piracy. A meta- morphic generator is used to create morphed copies of a base piece of software. A hidden Markov Model is trained on the opcode sequences extracted from these mor- phed copies. The trained model is then used to score suspect software to determine its similarity to the base software. A high score indicates that the suspect software may be a modified version of the base software and, therefore, further investigation is warranted. In contrast, a low score indicates that the suspect software differs sig- nificantly from the base software. We show that our approach is robust, in the sense that the base software must be extensively modified before it is not detected

Robust Watermarking using Hidden Markov Models

Software piracy is the unauthorized copying or distribution of software. It is a growing problem that results in annual losses in the billions of dollars. Prevention is a difficult problem since digital documents are easy to copy and distribute. Watermarking is a possible defense against software piracy. A software watermark consists of information embedded in the software, which allows it to be identified. A watermark can act as a deterrent to unauthorized copying, since it can be used to provide evidence for legal action against those responsible for piracy.In this project, we present a novel software watermarking scheme that is inspired by the success of previous research focused on detecting metamorphic viruses. We use a trained hidden Markov model (HMM) to detect a specific copy of software. We give experimental results that show our scheme is robust. That is, we can identify the original software even after it has been extensively modified, as might occur as part of an attack on the watermarking scheme

Are You Tampering With My Data?

We propose a novel approach towards adversarial attacks on neural networks (NN), focusing on tampering the data used for training instead of generating attacks on trained models. Our network-agnostic method creates a backdoor during training which can be exploited at test time to force a neural network to exhibit abnormal behaviour. We demonstrate on two widely used datasets (CIFAR-10 and SVHN) that a universal modification of just one pixel per image for all the images of a class in the training set is enough to corrupt the training procedure of several state-of-the-art deep neural networks causing the networks to misclassify any images to which the modification is applied. Our aim is to bring to the attention of the machine learning community, the possibility that even learning-based methods that are personally trained on public datasets can be subject to attacks by a skillful adversary.Comment: 18 page

A coding approach for detection of tampering in write-once optical disks

We present coding methods for protecting against tampering of write-once optical disks, which turns them into a secure digital medium for applications where critical information must be stored in a way that prevents or allows detection of an attempt at falsification. Our method involves adding a small amount of redundancy to a modulated sector of data. This extra redundancy is not used for normal operation, but can be used for determining, say, as a testimony in court, that a disk has not been tampered with