Inheritance Forgery

Many venerable norms in inheritance law were designed to prevent forgery. Most prominently, since 1837, the Wills Act has required testators to express their last wishes in a signed and witnessed writing. Likewise, the court-supervised probate process helped ensure that a donative instrument was genuine and that assets passed to their rightful owners. But in the mid-twentieth century, concern about forgery waned. Based in part on the perception that counterfeit estate plans are rare, several states relaxed the Wills Act and authorized new formalities for notarized and even digital wills. In addition, lawmakers encouraged owners to bypass probate altogether by transmitting wealth through devices such as life insurance and transfer-on-death deeds. This Article offers a fresh look at inheritance-related forgery. Cutting against the conventional wisdom, it discovers that counterfeit donative instruments are a serious problem. Using reported cases, empirical research, grand jury investigations, and media stories, it reveals that courts routinely adjudicate credible claims that wills, deeds, and life insurance beneficiary designations are illegitimate. The Article then argues that the persistence of inheritance-related forgeries casts doubt on the wisdom of some recent innovations, including statutes that permit notarized and electronic wills. The Article also challenges well-established inheritance law norms, including the litigation presumptions in will-forgery contests, the widespread practice of rubber-stamping deeds, and the delegation of responsibility for authenticating a nonprobate transfer to private companies. Finally, the Article outlines reforms to modernize succession while remaining sensitive to the risks of forgery

Camera-based Image Forgery Localization using Convolutional Neural Networks

Camera fingerprints are precious tools for a number of image forensics tasks. A well-known example is the photo response non-uniformity (PRNU) noise pattern, a powerful device fingerprint. Here, to address the image forgery localization problem, we rely on noiseprint, a recently proposed CNN-based camera model fingerprint. The CNN is trained to minimize the distance between same-model patches, and maximize the distance otherwise. As a result, the noiseprint accounts for model-related artifacts just like the PRNU accounts for device-related non-uniformities. However, unlike the PRNU, it is only mildly affected by residuals of high-level scene content. The experiments show that the proposed noiseprint-based forgery localization method improves over the PRNU-based reference