Tampering Rates Research Articles

AuSR3: A new block mapping technique for image authentication and self-recovery to avoid the tamper coincidence problem

This paper proposes a new block mapping technique for image authentication and self-recovery designed to avoid the tamper coincidence problem called the AuSR3. The tamper coincidence problem can arise when modifications to an image affect the original block and its recovery data, resulting in the inability to recover the tampered region of the image. The new block mapping technique ensures that the recovery data of a block is embedded into the most distant location possible, minimizing the tamper coincidence problem. In addition, the improved LSB shifting algorithm is employed to embed the watermark data consisting of authentication and recovery data. The experimental result shows that the AuSR3 can produce high-quality watermarked images across various datasets with average PSNR values of 46.2 dB, which improved by 2.1 dB compared to the LSB replacement technique. The new block mapping technique avoids the tamper coincidence problem by up to 25% tampering rates. It contributes to the high-quality recovered image with a PSNR and SSIM value of 39.10 dB and 0.9944, respectively, on a 10% tampering rate on the USC-SIPI dataset.

DNN self-embedding watermarking: Towards tampering detection and parameter recovery for deep neural network

• The watermark not only has large capacity, but also has little impact on the DNNs. • The watermark is so fragile that any change to model parameters can be detected. • The scheme can recover the tampered parameters by extracting the watermark. In recent years, a large number of deep neural networks (DNN) models have been built and deployed, which need to be protected against malicious tampering by the adversary. This work is the first to propose a recoverable, self-embedding fragile watermarking scheme for DNN models to protect the model integrity. This scheme can not only identify and locate the tampered parameter blocks in the model, but can also recover the damaged parameters accurately. Detailedly, through exploiting the characteristics of the to-be-protected DNN model, the authentication data and recovery data are generated, and then the reference sharing mechanism is used to embed these data into the model without affecting its original functionality, which can realize the model parameter recovery under different tampering rates. Experimental results demonstrate that, the proposed scheme can achieve satisfactory performance of tampering detection and parameter recovery with low device requirements and can be effectively adaptable to a variety of existing DNNs.

Color Image Self-Recovery and Tampering Detection Scheme Based on Fragile Watermarking with High Recovery Capability

In this paper, a fragile watermarking scheme for color image authentication and self-recovery with high tampering rates is proposed. The original image is sub-sampled and divided into non-overlapping blocks, where a watermark used for recovery purposes is generated for each one of them. Additionally, for each recovery watermark, the bitwise exclusive OR (XOR) operation is applied to obtain a single bit for the block authentication procedure. The embedding and extraction process can be implemented in three variants (1-LSB, 2-LSB or 3-LSB) to solve the tampering coincidence problem (TCP). Three, six or nine copies of the generated watermarks can be embedded according to the variant process. Additionally, the embedding stage is implemented in a bit adjustment phase, increasing the watermarked image quality. A particular procedure is applied during a post-processing step to detect the regions affected by the TCP in each recovery watermark, where a single faithful image used for recovery is generated. In addition, we involve an inpainting algorithm to fill the blocks that have been tampered with, significantly increasing the recovery image quality. Simulation results show that the proposed framework demonstrates higher quality for the watermarked images and an efficient ability to reconstruct tampered image regions with extremely high rates (up to 90%). The novel self-recovery scheme has confirmed superior performance in reconstructing altered image regions in terms of objective criteria values and subjective visual perception via the human visual system against other state-of-the-art approaches.

Detection and Recovery of Higher Tampered Images Using Novel Feature and Compression Strategy

Due to the availability of powerful image-editing software and the growing amount of multimedia data that is transmitted via the Internet, integrity verifications and confidentiality of the data are becoming critical issues. However, currently, the accuracy of detecting and the recovery capability of the tampered images by the existing methods through watermarking strategy is still not at the required level, especially at a higher tampered rate. This paper proposes a new blind and fragile watermarking method to detect tampering and better recovery of tampered images. To improve the quality of both the watermarked and the recovered images, a new feature extraction scheme is introduced which will produce a short but comprehensive recovery code using a new compression strategy. If a block in the image tampers, the proposed embedded feature allows the original data to be extracted for recovery. To overcome tamper coincidence, every block’s watermarked data contains not only the recovery code belonging to the block itself but also its neighbor’s data as a second layer of recovery. Various size blocks were investigated to see the performance and compare their efficiency for recovering an image after different tampering rates. The test showed the smaller block sizes may be more suitable for locating tampering, where the bigger ones are more suitable when the tampering rate is higher. The bigger block sizes in the proposed method can recover an image even after a 60% tampering rate with high quality (more than 31 dB). The experimental results prove that the proposed method can have better efficiency for detecting tampering, and recovery of the original image, compared to the relevant existing methods.

Self-Embedding Watermarking Algorithm Under High Tampering Rates

A self-embedding watermarking algorithm based on a reference sharing mechanism that has recovery ability under high tampering rates is proposed in this paper. In existing methods, the image recovery ability is often limited under an increasing tampering rate. The proposed method overcomes this limitation by seeking an approximate solution for each character-bit group tampered with. Moreover, the priority assignment of such groups can enhance the accuracy of tampering recovery. Based on this, by using the method proposed in this paper, recovered images always maintain excellent visual quality even at relatively high tampering rates. The experimental results demonstrate the efficacy of the proposed method.

Image tamper detection and multi-scale self-recovery using reference embedding with multi-rate data protection

This paper proposes a multi-scale self-recovery (MSSR) approach to protect images against content forgery. The main idea is to provide more resistance against image tampering while enabling the recovery process in a multi-scale quality manner. In the proposed approach, the reference data composed of several parts and each part is protected by a channel coding rate according to its importance. The first part, which is used to reconstruct a rough approximation of the original image, is highly protected in order to resist against higher tampering rates. Other parts are protected with lower rates according to their importance leading to lower tolerable tampering rate (TTR), but the higher quality of the recovered images. The proposed MSSR approach is an efficient solution for the main disadvantage of the current methods, which either recover a tampered image in low tampering rates or fails when tampering rate is above the TTR value. The simulation results on 10000 test images represent the efficiency of the multi-scale self-recovery feature of the proposed approach in comparison with the existing methods.

Digital image self-recovery algorithm based on improved joint source-channel coding optimizer

The purpose of the digital image self-recovery is to restore high quality images as much as possible when the image is tampered. Existing algorithms can only achieve high recovered image quality with tiny tampering rate. To obtain high recovered image quality with large tampering rate, this paper proposes a digital image self-recovery algorithm based on improved JSCC (Joint Source-Channel Coding) optimizer. The algorithm performs quadtree decomposition of original grayscale image corresponding to different decomposition factors γ and performs bit-plane layering according to the block class obtained by quadtree decomposition. Size of each bit-plane is the number of pixels of each block class. Then, the original image is compressed by SPIHT, and the compressed bit-stream of SPIHT is segmented into bit-plane according to the size in order. The bit-planes are protected by different RS (Reed-Solomon) coders to get optimal decomposition result of corresponding γ. Finally, JSCC optimization is designed to get an optimal quality of recovered image. Experimental results show that, using our algorithm, for 2-LSB embedding, when the tampering rate is less the minimum TTR (Tolerable Tempering Rate), the PSNR is improved by 4.93dB. When the tampering rate is larger the minimum TTR, the PSNR is improved by 2dB. When 3-LSB watermark are embedded, the PSNR of recovered image is improved by 2.58dB on average. It shows that our improved optimizer effectively improves the quality of the recovered image at high tampering rates, compared with the similar algorithms.

Watermarking Based on Compressive Sensing for Digital Speech Detection and Recovery †.

In this paper, a novel imperceptible, fragile and blind watermark scheme is proposed for speech tampering detection and self-recovery. The embedded watermark data for content recovery is calculated from the original discrete cosine transform (DCT) coefficients of host speech. The watermark information is shared in a frames-group instead of stored in one frame. The scheme trades off between the data waste problem and the tampering coincidence problem. When a part of a watermarked speech signal is tampered with, one can accurately localize the tampered area, the watermark data in the area without any modification still can be extracted. Then, a compressive sensing technique is employed to retrieve the coefficients by exploiting the sparseness in the DCT domain. The smaller the tampered the area, the better quality of the recovered signal is. Experimental results show that the watermarked signal is imperceptible, and the recovered signal is intelligible for high tampering rates of up to 47.6%. A deep learning-based enhancement method is also proposed and implemented to increase the SNR of recovered speech signal.

Hierarchical recovery for tampered images based on watermark self-embedding

In this paper, we propose a new self-embedding watermarking scheme with hierarchical recovery capability. The binary bits in the adopted MSB layers are scrambled and individually interleaved with different extension ratios according to their importance to image visual quality. The interleaved data, which are regarded as reference bits for tampering recovery, are segmented into a series of groups corresponding to the divided non-overlapping blocks, and then embedded into the LSB layers of blocks together with authentication bits of tampering detection. Because the extension ratios of MSB-layer bits are based on the hierarchical mechanism, the efficiency of reference bits is increased, and higher MSB layers of tampered regions have greater probabilities to be recovered than lower MSB layers, which can improve the visual quality recovered results, especially for larger tampering rates. Experimental results demonstrate the effectiveness and superiority of the proposed scheme compared with some of state-of-the-art schemes.

Self-embedding fragile watermarking based on reference-data interleaving and adaptive selection of embedding mode

In this paper, we propose a novel self-embedding fragile image watermarking scheme for tampering recovery based on reference-data interleaving mechanism and adaptive selection of embedding mode. During watermark embedding, reference bits are derived from the interleaved, scrambled MSB bits of original image, and then are combined with authentication bits to form the watermark bits for LSB embedding. Different with the reported schemes with the fixed embedding mode, the proposed scheme not only has two types of embedding modes, i.e., overlapping-free embedding and overlapping embedding, but also utilizes the adaptively flexible numbers of MSB and LSB layers to achieve satisfactory performances for different tampering rates. Also, detailed analyses are given to provide the theoretical values of watermarked-image quality, perfect recovery probability, and recovered-image quality, which are used to conclude the optimal choice of embedding modes. Experimental results show the effectiveness and superiority of the proposed scheme compared with some state-of-the-arts schemes.

Towards Practical Self-Embedding for JPEG-Compressed Digital Images

This paper deals with the design of a practical self-recovery mechanism for lossy compressed JPEG images. We extend a recently proposed model of the content reconstruction problem based on digital fountain codes to take into account the impact of emerging watermark extraction and block classification errors. In contrast to existing methods, our scheme guarantees a high and stable level of reconstruction quality. Instead of introducing reconstruction artifacts, emerging watermark extraction errors penalize the achievable tampering rates. We introduce new mechanisms that allow for handling high-resolution and color images efficiently. In order to analyze the behavior of our scheme, we derive an improved model to calculate the reconstruction success probability. We introduce a new hybrid mechanism for spreading the reference information over the entire image, which allows to find a good balance between the achievable tampering rates and the computational complexity. Such an approach reduced the watermark embedding time from the order of several minutes to the order of single seconds, even on mobile devices.

A Watermarking Method for Digital Speech Self-Recovery

Authentication and tampering detection of the digital signals is one of the main applications of the digital watermarking. Recently, watermarking algorithms for digital images are developed to not only detect the image tampering, but also to recover the lost content to some extent. In this paper, a new watermarking scheme is introduced to generate digital self-embedding speech signals enjoying the self-recovery feature. For this purpose, the compressed version of the speech signal generated by a speech codec and protected against the tampering by the proper channel coding is embedded into the original speech signal. Experimental results show that the self-embedding speech signal is recoverable with proper speech quality for high tampering rates, without significant loss in the quality of the original speech signal.