Image sharing chain detection aims to recover the sharing history of an image downloaded from online social networks (OSNs), including the ever-shared OSNs and their orders, which is an important task in the multimedia forensics community. Most of the existing algorithms directly treat the sharing chain detection as a classification problem by simply assigning a unique label to each sharing chain. Such a strategy though seems straightforward, it ignores the inherent properties of the sharing chain which can be regarded as a time sequence that carries the sharing history of an online image. In this paper, we suggest a new sharing chain detection framework via Sequence-to-Sequence (Seq2Seq) model. Different from previous classification based approaches, our model detects the sharing chain of online image progressively via a decoder. This progressive manner can fully utilize the decoded chain, which is embedded into a series of learned representations. Experimental results show that our method can detect sharing chains involving up to three OSNs, and exhibits much better performance than conventional ones.

Image restoration is a fundamental task in low-level computer vision. Most existing algorithms assume that the input image has a single known degradation type. In reality, images usually contain multiple degradations, making the restoration challenging. Though recent works restore the multiple degraded images, they assume that the degradation history is known. Obviously, such an ideal assumption often does not hold in real applications. This work proposes a novel restoration framework for multiple degraded images via degradation history estimation. Specifically, we first develop a sequential model to estimate the degradation history, including both the degradation operation chain and the corresponding parameters. By resorting to designed self-attention and cross-attention mechanisms, our method can effectively model the correlation of the input image, degradation operation chain, and parameters. Then, we apply our estimation framework for the multiple degraded image restoration, without requiring the degradation history. Experiment results demonstrate much better performance than existing approaches.

The aim of operation chain detection for a given manipulated image is to reveal the operations involved and the order in which they were applied, which is significant for image processing and multimedia forensics. Currently,allexisting approaches simply treat image operation chain detection as a classification problem and consider only chains of at most two operations. Considering the complex interplay between operations and the exponentially increasing solution space, detecting longer operation chains is extremely challenging. To address this issue, in this work, we devise a new methodology for image operation chain detection. Different from existing approaches based on classification modeling, we strategically conduct operation chain detection within a machine translation framework. Specifically, the chain in our work is modeled as a sentence in a target language, with each possible operation represented by a word in that language. When executing chain detection, we propose first transforming the input image into a sentence in a latent source language from the learned deep features. Then, we propose translating the latent language into the target language within a machine translation framework and finally decoding all operations, arranged in order. Besides, a chain inversion strategy and a bi-directional modeling mechanism are developed to improve the detection performance. We further design a weighted cross-entropy loss to alleviate the problems presented by imbalance among chain lengths and chain categories. Our method can detect operation chains containing up to seven operations and obtains very promising results in various scenarios for the detection of both short and long chains.

Fragrance in sight" is an interactive art installation that correlates visual and olfactory sensations. Besides, the art installation presents human's synesthesia capabilities by artificial intelligence. Fragrance in sight" recognizes the clothing style of user by artificial intelligence, then matches the results to the composition and proportion of the perfume to generate a customized perfume. Ultimately, build an individual cognition at the olfactory level.

Image manipulation chain detection aims to identify the existence of involved operations and also their orders, playing an important role in multimedia forensics and image analysis. However,all the existing algorithms model the manipulation chain detection as a classification problem, and can only detect chains containing up to two operations. Due to the exponentially increased solution space and the complex interactions among operations, how to reveal a long chain from a processed image remains a long-standing problem in the multimedia forensic community. To address this challenge, in this paper, we propose a new direction for manipulation chain detection. Different from previous works, we treat the manipulation chain detection as a machine translation problem rather than a classification one, where we model the chains as the sentences of a target language, and each word serves as one possible image operation. Specifically, we first transform the manipulated image into a deep feature space, and further model the traces left by the manipulation chain as a sentence of a latent source language. Then, we propose to detect the manipulation chain through learning the mapping from the source language to the target one under a machine translation framework. Our method can detect manipulation chains consisting of up to five operations, and we obtain promising results on both the short-chain detection and the long-chain detection.