The $Y_t$ are very unlikely to be independent random variables since
$Y_t = X_t-X_{t-1}$ and $Y_{t+1} = X_{t+1} - X_t$ both are functions of
$X_t$. So you cannot assume independence: the burden of proof is on
you to persuade other people by reasoned argument that $Y_t$ and
$Y_{t+1}$ are independent. If you want to use some statistical method
such as hypothesis testing to provide support (not proof) for your thesis,
then you need to set it up so that is the null hypothesis that $Y_t$ and $Y_{t+1}$
are dependent random variables and you need to be able to reject the
null definitively. The way you have it, your null hypothesis is that the random
variables are independent. Remember that failing to reject your null
hypothesis is by no means
a persuasive "proof" that your null hypothesis is true. A failure to
reject the null is not the same as a whole-hearted embrace of the
null.
I will give a proof that the $Y_t$'s are not independent by proving
that they are correlated random variables. Let
$R_X(t) = \text{cov}(X_{\tau}, X_{t+\tau})$ be the autocovariance function
of the $X_t$ stochastic process or time series. Then, the autocovariance
function of the $Y_t$ process is
$$\begin{align*}
R_Y(t)& = \text{cov}(Y_\tau, Y_{t+\tau})\\
&=\text{cov}(X_\tau-X_{\tau-1},X_{t+\tau}-X_{t+\tau-1})\\
&= \text{cov}(X_\tau, X_{t+\tau}) - \text{cov}(X_\tau, X_{t+\tau-1})
- \text{cov}(X_{\tau-1}, X_{t+\tau}) + \text{cov}(X_{\tau-1},X_{t+\tau-1})\\
&= R_X(t) - R_X(t-1) - R_X(t+1) + R_X(t)\\
&= 2R_X(t) - R_X(t-1) - R_X(t+1)
\end{align*}$$
that is, the $R_Y$ sequence is the convolution of the $R_X$
sequence and the autocorrelation function of the transformation
sequence $h = (1,-1)$ which is $R_h = (-1,2,-1)$. Thus,
even if the $X_t$ are an iid sequence so that $R_X(t) = 0$
for all $t\neq 0$, it cannot be that $R_Y(t)=0$ for all $t\neq 0$.
At the very least, $R_Y(\pm 1) = -R_X(0) \neq 0$.
So, while the $Y_t$'s can be identically distributed, they are not
independent. Call them id if you wish, but not iid.
If your tests are not revealing a large correlation at lag $1$,
that is fine. You should not be trying to reject the hypothesis
that $Y_t$ and $Y_{t-1}$ are independent, but rather to reject the
hypothesis that $Y_{t}$ and $Y_{t-1}$ are correlated, and to
reject this hypothesis is reasonable only if the correlation at
lag $1$ is very close to $0$. "Not large" is not good enough:
it should be negligibly small.