Revenue recycling and the welfare effects of road pricing

The authors explore the interaction between taxes on work-related traffic congestion and preexisting distortionary taxes in the labor market. A congestion tax raises the overall costs of commuting to work and discourages labor force participation at the margin when revenues are returned in lump-sum transfers. The resulting efficiency loss in the labor market can be larger that the Pigouvian efficiency gains from internalizing the congestion externality. By contrast, if congestion tax revenues are used to reduce labor taxes, the net impact on the labor supply is positive and the efficiency gain in the labor market can raise the overall welfare gains of the congestion tax by as much as 100 percent. Recycling congestion tax revenues in public transit subsidies produces a positive, but smaller, impact on the labor supply. In short, the authors'results indicate that the presence of preexisting tax distortions, and the form of revenue recycling, can crucially affect the size - and possibly even the sign - of the welfare effect of road pricing schemes. The efficiency gains from recycling congestion tax revenues in other tax reductions can amount to several times the Pigouvian welfare gains from congestion reduction.Public Sector Economics&Finance,Economic Theory&Research,Labor Policies,Environmental Economics&Policies,Banks&Banking Reform,Environmental Economics&Policies,Public Sector Economics&Finance,Economic Theory&Research,Banks&Banking Reform,Municipal Financial Management

Detecting replay attacks in audiovisual identity verification

We describe an algorithm that detects a lack of correspondence between speech and lip motion by detecting and monitoring the degree of synchrony between live audio and visual signals. It is simple, effective, and computationally inexpensive; providing a useful degree of robustness against basic replay attacks and against speech or image forgeries. The method is based on a cross-correlation analysis between two streams of features, one from the audio signal and the other from the image sequence. We argue that such an algorithm forms an effective first barrier against several kinds of replay attack that would defeat existing verification systems based on standard multimodal fusion techniques. In order to provide an evaluation mechanism for the new technique we have augmented the protocols that accompany the BANCA multimedia corpus by defining new scenarios. We obtain 0% equal-error rate (EER) on the simplest scenario and 35% on a more challenging one

Detecting replay attacks in audiovisual identity verification

We describe an algorithm that detects a lack of correspondence between speech and lip motion by detecting and monitoring the degree of synchrony between live audio and visual signals. It is simple, effective, and computationally inexpensive; providing a useful degree of robustness against basic replay attacks and against speech or image forgeries. The method is based on a cross-correlation analysis between two streams of features, one from the audio signal and the other from the image sequence. We argue that such an algorithm forms an effective first barrier against several kinds of replay attack that would defeat existing verification systems based on standard multimodal fusion techniques. In order to provide an evaluation mechanism for the new technique we have augmented the protocols that accompany the BANCA multimedia corpus by defining new scenarios. We obtain 0% equal-error rate (EER) on the simplest scenario and 35% on a more challenging one