Semantics-based software watermarking by abstract interpretation

Software watermarking is a software protection technique used to defend the intellectual property of proprietary code. In particular, software watermarking aims at preventing software piracy by embedding a signature, i.e. an identier reliably representing the owner, in the code. When an illegal copy is made, the owner can claim his/her identity by extracting the signature. It is important to hide the signature in the program in order to make it dicult for the attacker to detect, tamper or remove it. In this work we present a formal framework for software watermarking, based on program semantics and abstract interpretation, where attackers are modeled as abstract interpreters. In this setting we can prove that the ability to identify signatures can be modeled as a completeness property of the attackers in the abstract interpretation framework. Indeed, hiding a signature in the code corresponds to embed it as a semantic property that can be retrieved only by attackers that are complete for it. Any abstract interpreter that is not complete for the property specifying the signature cannot detect, tamper or remove it. We formalize in the proposed framework the major quality features of a software watermarking technique: secrecy, resilience, transparence and accuracy. This provides an unifying framework for interpreting both watermarking schemes and attacks, and it allows us to formally compare the quality of dierent watermarking techniques. Indeed, a large number of watermarking techniques exist in the literature and they are typically evaluated with respect to their secrecy, resilience, transparence and accuracy to attacks. Formally identifying the attacks for which a watermarking scheme is secret, resilient, transparent or accurate can be a complex and error-prone task, since attacks and watermarking schemes are typically dened in dierent settings and using dierent languages (e.g. program transformation vs. program analysis), complicating the task of comparing one against the others

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

Personal rights management (PRM) : enabling privacy rights in digital online media content

With ubiquitous use of digital camera devices, especially in mobile phones, privacy is no longer threatened by governments and companies only. The new technology creates a new threat by ordinary people, who now have the means to take and distribute pictures of one’s face at no risk and little cost in any situation in public and private spaces. Fast distribution via web based photo albums, online communities and web pages expose an individual’s private life to the public in unpreceeded ways. Social and legal measures are increasingly taken to deal with this problem. In practice however, they lack efficiency, as they are hard to enforce in practice. In this paper, we discuss a supportive infrastructure aiming for the distribution channel; as soon as the picture is publicly available, the exposed individual has a chance to find it and take proper action.Wir stellen ein System zur Wahrnehmung des Rechts am eigenen Bild bei der Veröffentlichung digitaler Fotos, zum Beispiel von Handykameras, im Internet vor. Zur Entdeckung der Veröffentlichung schlagen wir ein Watermarking-Verfahren vor, welches das Auffinden der Bilder durch die potentiell abgebildeten Personen ermöglicht, ohne die Rechte des Fotografen einzuschränken

Piracy prevention methods in software business

Abstract. There are various forms of piracy in software business, and many prevention techniques have been developed against them. Forms of software piracy are, for example, cracks and serials, softlifting and hard disk loading, internet piracy and software counterfeiting, mischanneling, reverse engineering, and tampering. There are various prevention methods that target these types of piracy, although all of these methods have been broken. The piracy prevention measures can be divided into ethical, legal, and technical measures. Technical measures include measures like obfuscation and tamper-proofing, for example. However, relying on a single method does not provide complete protection from attacks against intellectual property, so companies wishing to protect their product should consider combining multiple methods of piracy prevention

A novel semi-fragile forensic watermarking scheme for remote sensing images

Peer-reviewedA semi-fragile watermarking scheme for multiple band images is presented. We propose to embed a mark into remote sensing images applying a tree structured vector quantization approach to the pixel signatures, instead of processing each band separately. The signature of themmultispectral or hyperspectral image is used to embed the mark in it order to detect any significant modification of the original image. The image is segmented into threedimensional blocks and a tree structured vector quantizer is built for each block. These trees are manipulated using an iterative algorithm until the resulting block satisfies a required criterion which establishes the embedded mark. The method is shown to be able to preserve the mark under lossy compression (above a given threshold) but, at the same time, it detects possibly forged blocks and their position in the whole image.Se presenta un esquema de marcas de agua semi-frágiles para múltiples imágenes de banda. Proponemos incorporar una marca en imágenes de detección remota, aplicando un enfoque de cuantización del vector de árbol estructurado con las definiciones de píxel, en lugar de procesar cada banda por separado. La firma de la imagen hiperespectral se utiliza para insertar la marca en el mismo orden para detectar cualquier modificación significativa de la imagen original. La imagen es segmentada en bloques tridimensionales y un cuantificador de vector de estructura de árbol se construye para cada bloque. Estos árboles son manipulados utilizando un algoritmo iteractivo hasta que el bloque resultante satisface un criterio necesario que establece la marca incrustada. El método se muestra para poder preservar la marca bajo compresión con pérdida (por encima de un umbral establecido) pero, al mismo tiempo, detecta posiblemente bloques forjados y su posición en la imagen entera.Es presenta un esquema de marques d'aigua semi-fràgils per a múltiples imatges de banda. Proposem incorporar una marca en imatges de detecció remota, aplicant un enfocament de quantització del vector d'arbre estructurat amb les definicions de píxel, en lloc de processar cada banda per separat. La signatura de la imatge hiperespectral s'utilitza per inserir la marca en el mateix ordre per detectar qualsevol modificació significativa de la imatge original. La imatge és segmentada en blocs tridimensionals i un quantificador de vector d'estructura d'arbre es construeix per a cada bloc. Aquests arbres són manipulats utilitzant un algoritme iteractiu fins que el bloc resultant satisfà un criteri necessari que estableix la marca incrustada. El mètode es mostra per poder preservar la marca sota compressió amb pèrdua (per sobre d'un llindar establert) però, al mateix temps, detecta possiblement blocs forjats i la seva posició en la imatge sencera