On the Effectiveness of Image Manipulation Detection in the Age of Social Media

Image manipulation detection algorithms designed to identify local anomalies often rely on the manipulated regions being ``sufficiently'' different from the rest of the non-tampered regions in the image. However, such anomalies might not be easily identifiable in high-quality manipulations, and their use is often based on the assumption that certain image phenomena are associated with the use of specific editing tools. This makes the task of manipulation detection hard in and of itself, with state-of-the-art detectors only being able to detect a limited number of manipulation types. More importantly, in cases where the anomaly assumption does not hold, the detection of false positives in otherwise non-manipulated images becomes a serious problem. To understand the current state of manipulation detection, we present an in-depth analysis of deep learning-based and learning-free methods, assessing their performance on different benchmark datasets containing tampered and non-tampered samples. We provide a comprehensive study of their suitability for detecting different manipulations as well as their robustness when presented with non-tampered data. Furthermore, we propose a novel deep learning-based pre-processing technique that accentuates the anomalies present in manipulated regions to make them more identifiable by a variety of manipulation detection methods. To this end, we introduce an anomaly enhancement loss that, when used with a residual architecture, improves the performance of different detection algorithms with a minimal introduction of false positives on the non-manipulated data. Lastly, we introduce an open-source manipulation detection toolkit comprising a number of standard detection algorithms

An Optimized Message Passing Framework for Parallel Implementation of Signal Processing Applications

Novel reconfigurable computing platforms enable efficient realizations of complex signal processing applications by allowing exploitation of parallelization resulting in high throughput in a cost-efficient way. However, the design of such systems poses various challenges due to the complexities posed by the applications themselves as well as the heterogeneous nature of the targeted platforms. One of the most significant challenges is communication between the various computing elements for parallel implementation. In this paper, we present a communication interface, called the signal passing interface (SPI), that attempts to overcome this challenge by integrating relevant properties of two different yet important paradigms in this context — dataflow and the message passing interface (MPI). SPI is targeted towards signal processing applications and, due to its careful specialization, more performance-efficient for their embedded implementation. It is also more easier and intuitive to use. Earlier, a preliminary version of SPI was presented [12] which was restricted to static dataflow behavior. Here, we present a more complete version of SPI with new features to address both static and dynamic dataflow behavior, and to provide new optimization techniques. We develop a hardware description language (HDL) realization of the SPI library, and demonstrate its functionality on the Xilinx Virtex-4 FPGA. Details of the HDL-based SPI library along with experiments with two signal processing applications on the FPGA are also presented. 1

SCCS: A Scalable Clustered Camera System for Multiple Object Tracking Communicating via Message Passing Interface

We introduce the Scalable Clustered Camera System, a peerto-peer multi-camera system for multi-object tracking, where different CPUs are used to process inputs from distinct cameras. Instead of transferring control of tracking jobs from one camera to another, each camera in our system performs its own tracking and keeps its own tracks for each target object, thus providing fault tolerance. A fast and robust tracking method is proposed to perform tracking on each camera view, while maintaining consistent labeling. In addition, we introduce a new communication protocol, where the decisions about when and with whom to communicate are made such that frequency and size of transmitted messages are minimized. This protocol incorporates variable synchronization capabilities, so as to allow flexibility with accuracy tradeoffs. We discuss our implementation, consisting of a parallel computing cluster, with communication between the cameras performed by MPI. We present experimental results which demonstrate the success of the proposed peer-to-peer multicamera tracking system, with accuracy of 95 % for a high frequency of synchronization, as well as a worst-case of 15 frames of latency in recovering correct labels at low synchronization frequencies. 1

On the Effectiveness of Image Manipulation Detection in the Age of Social Media

Image manipulation detection algorithms designed to identify local anomalies often rely on the manipulated regions being “sufficiently” different from the rest of the non-tampered regions in the image. However, such anomalies might not be easily identifiable in high-quality manipulations, and their use is often based on the assumption that certain image phenomena are associated with the use of specific editing tools. This makes the task of manipulation detection hard in and of itself, with state of the art detectors only being able to detect a limited number of manipulation types. More importantly, in cases where the anomaly assumption does not hold, the detection of false positives in otherwise non-manipulated images becomes a serious problem. To understand the current state of manipulation detection, we present an in-depth analysis of deep learning-based and learning-free methods, assessing their performance on different benchmark datasets containing tampered and non-tampered samples. We provide a comprehensive study of their suitability for detecting different manipulations as well as their robustness when presented with non-tampered data. Furthermore, we propose a novel deep learning-based pre-processing technique that accentuates the anomalies present in manipulated regions to make them more identifiable by a variety of manipulation detection methods. To this end, we introduce an anomaly enhancement loss that, when used with a residual architecture, improves the performance of different detection algorithms with a minimal introduction of false positives on the non-manipulated data. Lastly, we introduce an open source manipulation detection toolkit comprising a number of standard detection algorithms

A Scalable Clustered Camera System for Multiple Object Tracking

Reliable and efficient tracking of objects by multiple cameras is an important and challenging problem, which finds wide-ranging application areas. Most existing systems assume that data from multiple cameras is processed on a single processing unit or by a centralized server. However, these approaches are neither scalable nor fault tolerant. We propose multicamera algorithms that operate on peer-to-peer computing systems. Peer-to-peer vision systems require codesign of image processing and distributed computing algorithms as well as sophisticated communication protocols, which should be carefully designed and verified to avoid deadlocks and other problems. This paper introduces the scalable clustered camera system, which is a peer-to-peer multicamera system for multiple object tracking. Instead of transferring control of tracking jobs from one camera to another, each camera in the presented system performs its own tracking, keeping its own trajectories for each target object, which provides fault tolerance. A fast and robust tracking algorithm is proposed to perform tracking on each camera view, while maintaining consistent labeling. In addition, a novel communication protocol is introduced, which can handle the problems caused by communication delays and different processor loads and speeds, and incorporates variable synchronization capabilities, so as to allow flexibility with accuracy tradeoffs. This protocol was exhaustively verified by using the SPIN verification tool. The success of the proposed system is demonstrated on different scenarios captured by multiple cameras placed in different setups. Also, simulation and verification results for the protocol are presented

Symmetric Table Addition Methods for Neural Network

Symmetric table addition methods (STAMs) approximate functions by performing parallel table lookups, followed by multioperand addition. STAMs require significantly less memory than direct table lookups and are faster than piecewise linear approximations. This paper investigates the application of STAMs to the sigmoid function and its derivative, which are commonly used in artificial neural networks. Compared to direct table lookups, STAMs require between 23 and 41 times less memory for sigmoid and between 24 and 46 times less memory for sigmoid's derivative, when the input operand size is 16 bits and the output precision is 12 bits