Large Feature Mining With Ensemble Learning for Image Forgery Detection

The detection of different types of forgery manipulation including seam-carving in JPEG images is a hot spot in image forensics. Seam carving was originally designed for content-aware image resizing. It is also being used for forgery manipulation. It is still very challenging to effectively identify the seam carving forgery under recompression. To address the highly challenging detection problems, this chapter introduces an effective approach with large feature mining. Ensemble learning is used to deal with the high dimensionality and to avoid overfitting that may occur with some traditional learning classifier for the detection. The experimental results validate the efficacy of proposed approach to detecting JPEG double compression and exposing the seam-carving forgery while the JPEG recompression is proceeded at the same quality and a lower quality, which is generally much harder for traditional detection methods. The methodology introduced in this chapter provides a strategy and realistic approach to resolve the highly challenging problems in image forensics.

A Two-Stage Cascaded Detection Scheme for Double HEVC Compression Based on Temporal Inconsistency

Nowadays, verifying the integrity of digital videos is significant especially for applications about multimedia communication. In video forensics, detection of double compression can be treated as the first step to analyze whether a suspicious video undergoes any tampering operations. In the last decade, numerous detection methods have been proposed to address this issue, but most existing methods design a universal detector which is hard to handle various recompression settings efficiently. In this work, we found that the statistics of different Coding Unit (CU) types have dissimilar properties when original videos are recompressed by the increased and decreased bit rates. It motivates us to propose a two-stage cascaded detection scheme for double HEVC compression based on temporal inconsistency to overcome limitations of existing methods. For a given video, CU information maps are extracted from each short-time video clip using our proposed value mapping strategy. In the first detection stage, a compact feature is extracted based on the distribution of different CU types and Kullback–Leibler divergence between temporally adjacent frames. This detection feature is fed into the Support Vector Machine classifier to identify abnormal frames with the increased bit rate. In the second stage, a shallow convolutional neural network equipped with dense connections is designed carefully to learn robust spatiotemporal representations, which can identify abnormal frames with the decreased bit rate whose forensic traces are less detectable. In experiments, the proposed method can achieve more promising detection accuracy compared with several state-of-the-art methods under various coding parameter settings, especially when the original video is recompressed with a low quality (e.g., more than 8%).

Hybrid Brayton Multi-Stage Concentrated Solar Power Plant Energy and Exergy Performance Study

Hybrid Brayton concentrated solar power (CSP) plants have been gaining attention in the last decade upon many advantages regarding the use of traditional generation technologies combined with renewable energy sources. However, some technical and economic issues must be solved to allow its widespread use. Research and development efforts are deemed essential to the study of factors that constrain cycle performance looking to increase its efficiency, reducing fuel consumption, and decreasing emissions. This study presents the performance evaluation of a hybrid multi-stage CSP plant considering specific environmental conditions to attain the factor that constrains its optimal performance. Overall energy and exergy plant efficiencies are analyzed, considering an arbitrary number of stages. For instance, a double compression expansion hybrid CSP plant shows the overall energy efficiency of 32% larger, a 30% higher exergy efficiency, and a fuel conversion rate around 18% larger when compared with a single-stage CSP plant.